Open Access

Structure of pauses in speech in the context of speaker verification and classification of speech type

  • Magdalena Igras-Cybulska1Email author,
  • Bartosz Ziółko1, 2,
  • Piotr Żelasko1, 2 and
  • Marcin Witkowski1, 2
EURASIP Journal on Audio, Speech, and Music Processing20162016:18

https://doi.org/10.1186/s13636-016-0096-7

Received: 28 November 2015

Accepted: 28 October 2016

Published: 9 November 2016

Abstract

Statistics of pauses appearing in Polish as a potential source of biometry information for automatic speaker recognition were described. The usage of three main types of acoustic pauses (silent, filled and breath pauses) and syntactic pauses (punctuation marks in speech transcripts) was investigated quantitatively in three types of spontaneous speech (presentations, simultaneous interpretation and radio interviews) and read speech (audio books). Selected parameters of pauses extracted for each speaker separately or for speaker groups were examined statistically to verify usefulness of information on pauses for speaker recognition and speaker profile estimation. Quantity and duration of filled pauses, audible breaths, and correlation between the temporal structure of speech and the syntax structure of the spoken language were the features which characterize speakers most. The experiment of using pauses in speaker biometry system (using Universal Background Model and i-vectors) resulted in 30 % equal error rate. Including pause-related features to the baseline Mel-frequency cepstral coefficient system has not significantly improved its performance. In the experiment with automatic recognition of three types of spontaneous speech, we achieved 78 % accuracy, using GMM classifier. Silent pause-related features allowed distinguishing between read and spontaneous speech by extreme gradient boosting with 75 % accuracy.

Keywords

Pauses Fillers Punctuation Polish Biometry Spontaneous speech Speech classification

1 Introduction

A set of common disfluencies interferes with discourse consistency in spontaneous speech. The most important ones are filled pauses, restarts, changes of syntax during the utterance, and inclusions of intervening sentences. Within words, the most frequent disfluencies are repetitions, repairs and prolongations of conjunctives, prepositions, and final syllables. As far as human perception can focus on the meaning of the utterance and extract the desired information, an automatic speech recognition system literally recognizes whole acoustic content of the speech signal. As a result, the transcription is redundant with notation of disfluencies or slips of the tongue but diminished of the other types of information present in a signal, like punctuation. Pause detection is usually used only to extract voice activity regions for further processing or to remove undesirable disfluencies. On the other hand, the information on pauses properties patterns can significantly enrich high-level information of speech signal. In recent years, analysis of multi-layered linguistic and paralinguistic metadata of recordings received focused attention [1].

We assume that the pauses properties in speech signal are strongly individualized between speakers and influenced by situational context and cognitive task. This study aims to verify if the information on pauses can be useful for speaker biometry systems (experiment 1) and for recognition of different types of spontaneous speech (experiment 2) as well as distinguishing between read and spontaneous speech (experiment 3).

The information is meaningful for creating a speaker psycho-social profile. Additionally, it helps in discourse analysis for different kinds of situational context or linguistic task [27]. The types of speech differ in situational context, the task involving a cognitive load as well as level of spontaneity and have direct impact on speech fluency. For the first type, presentations prepared on a given subject represent typical informative speech in a formal situation. For the second type, oral translations performed by professional interpreters are partly imitative against the original speech. Simultaneity of listening and speaking engages complex cognitive functions. For the third type, radio interviews represents spontaneous speech extracted from dialogues with slow turn-taking, mainly storytelling (indicating more informal situation). Although the pauses in each type of speech were characterized in numerous analyses (e.g., [3335]), there has been a lack of automatic classification of speech type based on silent pause-related cues only. In a similar study dealing with three classes of spontaneous speech [32], a comparable accuracy was obtained, though much more features was utilized.

Research on the recognition of read and spontaneous speech can have an impact in the field of automatic assessment of speaker preparation for the task, and elocution abilities. In this application, a feedback on similarity to fluent read speech would help speaker improve their level of oratorical skills.

Modeling of pauses in spoken language can be also applied to a more natural-sounding speech synthesis systems. The impact of pauses analysis for speech technology is particularly important for spontaneous speech recognition, which remains a challenging task [28]. Some results of presented works have already been used for building pauses models for automatic speech recognition system which is developed in AGH University and Techmo [29].

The paper is organized as follows: in the rest of Section 1, the background of the pauses appearance and role in speech is presented and the state-of-the-art of speaker recognition systems is briefly discussed. Next, the collected database is described in Section 2. In Section 3, we summarize adopted method of database processing, features extraction and statistical tools. Section 4 contains results of our experiments, which are discussed in Section 5. The paper is concluded in Section 6.

1.1 Pauses

The research shows three types of acoustic pauses in spoken language. The most intuitive is silent pauses (s_p), as regions of signal where no voice activity is recorded.

The second types are filled pauses (f_p)—pseudo-words—that do not affect sentence meaning, like yyy, eee, hmm, mmm, ym, yh (in SAMPA notation: III, eee, xmm, mmm, Im, Ix) but perturb utterance fluency. The sounds of filled pauses are specific for language (in Polish, the most common are yyy/yh and mmm, while for English—um) and specific for speaker’s habits. They can appear even 10–20 per minute in case of inexperienced speakers.

The third sort of pauses that we consider are breath pauses (b_p). In case of normal physiological condition, the value of breath per minute is 12–20 while resting, and as prior work showed, it is 10–12 during speech production [2].

Considering the origin of the pause usage we marked out: (1) regular natural pauses caused by respiration activity (breath pauses), (2) irregular intentional pauses, purposely used as a stylistic form, especially by professional speakers (silent pauses), and (3) irregular, unintentional disfluencies, effects of uncertainty, hesitations or short reflections (acoustic events like silent pauses or filled pauses).

1.2 Pauses vs. paralinguistic information

Depending on the speaker and situational/social context, pauses may be characterized by different properties. One of them is a type of personality of the speaker and his speaking habits. Another important factor is speaker preparation for the task, level of oratorical skills, and elocution abilities. Durations of pauses depend also on the kind of linguistic task. One can easily assume that stress during speaking is an important factor dictating the frequency and lengths of pauses. Pauses can be also considered in the terms of performative aspects of speech. Filled pauses, among other disfluencies, were successfully used for recognition of three levels of spontaneity and applied to speaker role recognition with over 70 % precision [32]. Pauses were also described as traces of cognitive activity or a mirror of cognitive processes. In the situation of simultaneous interpretation, they were studied in [33] and [34].

Pause duration was reported to correlate with social attributes of speaker, even ones such as region, ethnicity, age, and gender [3]. Cross-cultural study of silent pauses in selected European languages (Polish was not included) revealed differences in pause durations between languages [4], but their distribution is usually similar and can be well estimated by bi-Gaussian model [5].

Some medical aspects of different types of pauses were investigated in context of affective state [6] and physical [7] or mental [8] condition of the speaker, e.g. schizophrenics make pauses around 10 % more often, which are also around 10 % longer [8].

In speech technology, information on pauses is used in majority of algorithms of automatic punctuation detection [9, 10]. It has been shown that 95 % of silent pauses longer than 350 ms are the sentence boundaries [11].

Pausing behavior in speech, although conditioned by articulatory processes, was proved to be partly related to cognitive processes [12]. It implies that it can be changed by learning. Nevertheless, the fact was proved only for the grammatical pauses, while for the ungrammatical ones the opposite was observed.

1.3 Speaker recognition

Speaker recognition is the process of analysis of the speaker identity based on voice characteristics. The main tasks of a speaker recognition system include verification and/or identification. The aim of identification is to choose one of many speakers based on a speech signal, whereas verification is the process of determination whether assigned speaker was chosen correctly. In line with the particular usage specification, those systems may be divided into text-dependent or text-independent. A text-dependent system assumes that recognition process is based on a specific fixed phrase, i.e., each analyzed recording contains the same sentence. In text-independent scenario, speakers may be identified or verified by a random utterance [13]. The second system is more challenging, since it is much more complicated due to phonetically mismatched voice samples in training and recognition phases.

Automatic speaker recognition systems consist of two main functionalities—enrollment and verification. During enrollment phase, voiceprint or model of a speaker is calculated based on extracted features from voice samples. Verification is based on comparison of processed input speech signal against the speaker model enrolled previously. There are many discriminative features that may be used to distinguish a speaker. Low-level features, like formants or energy, contain information connected with voice generation. Mel-frequency cepstral coefficients (MFCCs) are used most frequently to parameterize voice signals [14, 21, 24].

1.4 Pause-related features in speaker recognition task

In past several years, there has been an observable tendency to include prosodic features to speaker recognition problem. High-level features are associated with linguistic and behavioral characteristics of each speaker [15]. In majority of approaches, features related to pitch, energy, and segmental duration were investigated [1618] and by including these parameters, the system accuracy increased by about 10 %. Peskin et al. [16] experimented also with pause duration and frequency and found out that pause-related feature set was the least significant compared to other groups of prosodic features. In Sönmez et al. [17] experiment, pause duration was modeled with shifted exponential and together with voiced segments duration gave 3.5 % improvement in speaker recognition task. It was also proved that patterns of pauses in network traffic introduced when encoding an audio signal are speaker-specific, and that they are sufficient to weaken the anonymity of the speaker in encrypted voice communication [19].

Therefore, the goal of this research is to check if pauses may be used as one of the high-level factors and potentially improve existing systems. To the best of authors’ knowledge, the pauses features were not yet directly used in any speaker recognition system, in particular, in any Polish one.

2 Materials

The prepared corpus of spontaneous Polish speech consisted of different types of monologues in formal or semi-formal situations. Total duration of recordings is 120 min, including utterances of 30 speakers (16 male, 14 female). Among them, there are both experienced or professional speakers (politicians, professors, professional translators, radio interviewees) and inexperienced speakers (students) [38].

The first group of recordings (30 min) is formed by utterances from orations or public presentations: speeches and reports from European Parliament [20], sessions of a faculty council, students’ lectures, and reviews. All the speeches, although preceded by preparation of the speakers or supported by slides, were not read and are characterized by all the typical features of spontaneous speech.

The second part of the corpus (30 min) consisted of recordings of real-time translation of orations during European Parliament sessions [20]. This sort of utterances is specific kind of spontaneous speech, where the speech rate of the translator is determined by the style of the speaker being translated. However, they are situations of formularization of own utterance, which causes their spontaneous character and induces presence of imperfections specific for spontaneous speech.

The third type of recordings (60 min) is radio broadcasts, which were prepared by removing the voice of interviewer, leaving only the expressions of interviewees. The length of recordings after preparation was 10 min for each speaker (three females and three males).

Another corpus of read speech was prepared, for comparison with spontaneous speech and for evaluation tests. It consisted of recordings from audio books and AGH Audio-Visual Speech Database (50 speakers, 15 min of continuous speech for each speaker).

Since the recordings originate from different sources, they vary in quality, type, and level of background noise and SNR factor. Diversified conditions of recording (equipment, environment, transmission channel variability, and distance from the speaker to microphone) determined whether the signal contained events of our interest (e.g., recordings with low SNR or too big distance from mouth to microphone do not contain information on breath pauses).

The recordings were labeled with P for presentations/orations T for translations, R for radio dialogues, and A for audiobooks and other sources of read speech and described with number of the speaker and duration of utterance (in minutes).

3 Methods

3.1 Pauses tagging and annotation

First we transcribed orthographically the content of the recordings to clean (skipping disfluencies, filled pauses or repairs) and syntactically correct texts. On the basis of the observation of the process, the factors affecting the imprecision and ambiguity of inserting punctuation in the transcripts were collected. One of the impediments was ambiguous intonation, especially in case of inexperienced speakers. It manifested as “enumerating” tone of voice, which caused the speaker to preserve the same tone during commas and full stops. Another symptom was construction of multiple complex sentences with every clause starting with a conjunctive pronounced with extended phonation. In such cases, the decision of inserting comma or full stop remained subjective. When a speaker did not signalize the phrases and sentences border with their pronunciation, intonation or pauses, the punctuation was based on the meaning of the utterance. The last word of preceding sentence was often bonded with the first in the next one. In translators group we usually observed specific disorder of phonotactics involving artificial prolongations of whole words. Transposals of functional elements of sentences and reorganization of the sentence were also frequent events. It is common for inexperienced speakers to place intervening sentences during the speech or abusing certain words like let’s say, just, simply (language-specific conversational fillers/discourse makers).

For each transcription, the number of words, full stops, and commas were counted. Then, the statistics of sentences and phrases lengths were computed: mean length of a sentence and a phrase, as well as a mean number of words in sentences and phrases. Then, in the places of punctuations signs, occurrences of pauses were verified. When a full stop was signalized by a silent pause, the time was tagged as s_p. (similarly for commas - s_p,), filled pause - f_p. (commas - f_p,), b_p. for a breath pause (b_p, for commas). When no type of pause appeared, the place was tagged as n_p. (n_p,). The parameters were included in the feature vectors (Table 1). Time annotation of breaths and filled pauses were prepared manually with half-automated Annotator software. As a result, Master Label Files (mlf, HTK standard) were attached to each recording.
Table 1

Explanation of adopted abbreviations

Parameter

Description

Parameter

Description

s_p

Silent pause

f29

f_p(m) per minute

f_p(y)

Filled pause “yyy”

f30

% of f_p(m) time in recording

f_p(m)

Filled pause “mmm”

f31

mean f_p(m) duration [ms]

b_p

Breath pause

f32

f_p(m) duration std. dev. [ms]

f1

s_p ≥ 100 ms per minute

f33

f_p(m) duration .25 quantile [ms]

f2

% of s_p time in recording

f34

f_p(m) duration median [ms]

f3

mean s_p duration [ms]

f35

f_p(m) duration .75 quantile [ms]

f4

s_p duration std. dev. [ms]

f36

b_p per minute

f5

s_p duration .25 quantile [ms]

f37

% of b_p time in recording

f6

s_p duration median [ms]

f38

mean b_p duration [ms]

f7

s_p duration .75 quantile [ms]

f39

b_p duration std. dev. [ms]

f8

s_p ≥ 150 ms per minute

f40

b_p duration .25 quantile [ms]

f9

% of s_p time in recording

f41

b_p duration median [ms]

f10

mean s_p duration [ms]

f42

b_p duration .75 quantile [ms]

f11

s_p duration std. dev. [ms]

f43

#w/min - number of words per minute

f12

s_p duration .25 quantile [ms]

f44

#./min - number of sentence per minute

f13

s_p duration median [ms]

f45

Mean sentence duration [s]

f14

s_p duration .75 quantile [ms]

f46

#w/sent - number of words in sentence

f15

s_p ≥ 200 ms per minute

f47

#,/sent - number of phrases per minute

f16

% of s_p time in recording

f48

Mean phrase duration [s]

f17

mean s_p duration [ms]

f49

#w/phr - number of words per phrase

f18

s_p duration std. dev. [ms]

f50

% of full stops correlated with n_p

f19

s_p duration .25 quantile [ms]

f51

% of full stops correlated with s_p

f20

s_p duration median [ms]

f52

% of full stops correlated with f_p

f21

s_p duration .75 quantile [ms]

f53

% of full stops correlated with b_p

f22

f_p(y) per minute

f54

% of commas correlated with n_p

f23

% of f_p(y) time in recording

f55

% of commas correlated with s_p

f24

mean f_p(y) duration [ms]

f56

% of commas correlated with f_p

f25

f_p(y) duration std. dev. [ms]

f57

% of commas correlated with b_p

f26

f_p(y) duration .25 quantile [ms]

 

f27

f_p(y) duration median [ms]

 

f28

f_p(y) duration .75 quantile [ms]

 

3.2 Feature extraction

In order to find silent pauses in all recordings, an ITU-T G.729b compliant voice activity detector (VAD) was used, which relies on full band energy, low band energy, zero-crossing rate and a spectral measure to decide whether a 10-ms segment contains voice. Silent pauses were detected with different lower thresholds: 100, 150, and 200 ms.

For each speaker, the amount and duration of each type of pauses were used to calculate number of pauses per minute, percentage of pause duration in the recording, and mean pause duration with its standard deviation as well as its median and quartiles. The parameters are listed and explained in Table 1.

3.3 Statistical methods

Values of extracted features f n (where n = 1,…,P) were standardized according to the equation
$$ \overline{{f_n}_{,s}}=\frac{f_{n,s}-\mu \left({f}_n\right)}{\sigma \left({f}_n\right)}, $$
(1)

where \( \overline{f_{n,s}} \) is a normalized value of the feature f n for sth speaker, μ and σ are mean and standard deviation of the variable f n in the examined population.

The properties of speaker s are specified by vector p s  = f 1,s ,f 2,s ,…,f P,s of length P, and its distance to mean value vector p is computed as
$$ {\gamma}_s=\frac{1}{P}{\displaystyle \sum_{n=1}^P\left|\overline{f_{n,s}}\right|}. $$
(2)
In order to investigate potential correlation between speakers and parameters describing pauses in their speech, we computed correlation matrix
$$ Corr={\displaystyle \sum_{s=1}^N\Big(p-{p}_s}\Big){\left(p-{p}_s\right)}^T, $$
(3)
where p is an average vector for all speakers, and p s is a vector that characterizes sth speaker. In the experiment number of speakers N is equal to 30. We performed the operation for P = 57 parameters listed in Table 1.

For the parameters which could be obtained for every speaker in our corpus, (i.e., b_p and f_p(y) durations), we concluded an analysis of variance (ANOVA) in order to check the statistical significance of differences between speakers based upon only one of these parameters at a time. For clustering experiment, we used dendrogram method based on Euclidean metrics.

3.4 Evaluation methods

The scope of the first experiment was to verify if information on pauses can enhance a biometric verification system, therefore an i-vector based system was set up as a baseline to perform evaluation process. The i-vector approach assumes creation of a Universal Background Model (UBM) with a vast amount of data during setup phase. This process is performed by maximum likelihood estimation of a Gaussian Mixture Model (GMM), using an Expectation Maximization (EM) algorithm with K-Means initialization. The next stage of i-vector modeling is transition from the GMM supervector space into a low dimensional subspace, which is able to represent a whole utterance as a vector of coordinates, called the i-vector. To that end, a transformation matrix, called Total Variability matrix (TV), is estimated also using a maximum likelihood algorithm [30].

The aim of UBM is to represent common characteristics of all possible speakers, and the role of dimensionality reducing transformation is to select only the relevant ones for a given speaker. In enrollment process in the baseline system, the recording is first segmented to 20-ms frames and parameterized by Mel-frequency cepstral coefficients (MFCC), followed by feature warping [22]. Then, model of each system user is acquired by calculating frame posteriors using GMM-UBM and extracting i-vectors with a variational Bayes algorithm [30]. The enrolled model represents unique biometrical features of a given speaker. The Pauses system models each user by extracting prosodic features from a larger recording segment, and thus requires at least 1 min of audio for enrollment and verification.

The final system is acquired by combining the final scores of i-vector/MFCC and Pauses systems in evaluation process. Because of that, parameterization based on pauses was added as a parallel to i-vector system. General verification method is presented in Fig. 1. MFCC features extracted from an input signal are forwarded into i-vector extractor. Cosine distance scoring
Fig. 1

Verification process in pauses—MFCC biometric speaker verification evaluation system

$$ \mathrm{C}\mathrm{D}\mathrm{S}\left(w1,w2\right)=\frac{w1*w2}{\left\Vert w1\right\Vert \left\Vert w2\right\Vert }, $$
(4)
where w1 and w2 are i-vectors, was used to obtain a likelihood measure between verified and tested i-vector.

The final score of combined systems is computed with the Bosaris Toolkit [31].

Speaker verification system is a binary classifier, since it determines whether analyzed signal is or is not produced by user related to model stored in a system database. The output of the system is the information that user is a target (the analyzed model is the user model) or an impostor (the model was created in other user enrollment process). Evaluation is therefore based on analysis of target and impostor likelihood distributions. The more separated are those sets the better system works, since it is easier to choose a threshold that divides those sets. In general, expected value of target distribution is greater than the impostor one. Basing on likelihood distributions, it is possible to calculate cumulative distribution functions for targets and impostors. Those functions may be used to determine false positive ratio (FPR) and false negative ratio (FNR) functions which determine respectively the probability that an impostor is classified as a target and a target is classified as an impostor for a particular likelihood. Decision about particular threshold i.e. choice of operating point, is dependent on the use case of such system. Increasing the threshold results in lower FPR, but also means that more targets verification will fail. The value where FPR = FNR is called equal error rate (EER) and is widely used to determine performance of a verification system as a single parameter. Other operating points of a system, where FNR is not equal to FPR are commonly evaluated with detection error trade-off (DET) plots [39], which include miss probability (FNR) at vertical axis and false alarm probability (FPR) at horizontal one.

In the second experiment, we verified if pause-related features are useful for automatic classification of three types of spontaneous speech—types P, T, and R. Classification is performed with 3-components GMMs. We applied leave-one-out cross-validation where each time one speaker's recording was the testing one and all the others formed the training set. The goal of the third experiment was to automatically distinguish two classes: read (50 audio books) and spontaneous speech (27 recordings of types P, T and R). Several classifiers were tested: decision tree, logistic regression, support vector machine (SVM), random forest classifier and Extreme Gradient Boosting (XGBoost) [36], using Scikit-learn toolkit [37]. Again, a leave-one-out cross-validation was applied.

4 Results

4.1 Overall analysis of pauses appearance in speech

Speech rate in spontaneous monologues is about 117 words per minute (with standard deviation between speakers is about 24 words/min). Mean length of a sentence (containing average 18 words) was about 10 s, while mean length of a speech unit divided by punctuation (average 5 words) - 3.5 s. The results were similar for both orations/ presentations, real time translations and interviews (more results are presented in Table 2).
Table 2

Frequency of punctuation in transcripts: mean (standard deviation)

Recordings

Words per minute

Sentences per minute

Sentence duration [s]

Words per sentence

Phrases per minute

Phrase duration [s]

Words per phrase

Type P

11,9 (24,3)

6,2 (2,4)

10,9 (3,7)

19,6 (5,8)

19,4 (6,0)

3,3 (0,8)

4,4 (1,0)

Type T

117,2 (20,1)

6,6 (2,0)

10,0 (3,2)

19,1 (4,7)

14,6 (3,3)

4,3 (1,0)

4,6 (0,8)

Type R

129,7 (29,1)

9,9 (3,3)

6,7 (2,3)

13,9 (3,3)

16,1 (4,2)

2,5 (0,8)

5,4 (0,8)

All speakers

117,6 (23,8)

7,1 (2,8)

9,7 (3,5)

18,2 (5,3)

16,8 (5,1)

3,5 (1,1)

4,7 (0,9)

The most commonly used types of filled pauses are: prolonged “yyy” (50 %), short “yh” (41 %) and “mmm” (7 % of counts) (see Fig. 2). For the purpose of this research, we grouped together the “yyy” and “yh” categories and skipped other fillers, which are very rare (2 %).
Fig. 2

Different types of filled pauses and frequency of their occurrence in 1 h corpus [23]

As for acoustically registered breath pauses, average for a speaker was about 11 breaths per minute. Quantity of filled pauses in a minute of recordings was often surprisingly high, especially for inexperienced speakers (even above 10 per minute). Mean frequency of different types of pauses are compared in Table 3.
Table 3

Frequency of silent, breath, and filled pauses in recordings: mean (standard deviation)

Recordings

#(s_p > 100 ms)/min

#(s_p > 150 ms)/min

#(s_p > 200 ms)/min

#f_p(yyy)/min

#f_p(mmm)/min

#b_p/min

Type P

10,7 (7,5)

7,7 (6,1)

5,3 (4,7)

6,5 (5,5)

0,6 (0,9)

10,5 (3,9)

Type T

2,2 (2,4)

1,1 (1,1)

0,7 (0,9)

5,8 (3,5)

0,9 (1,0)

11,0 (3,8)

Type R

2,6 (3,9)

1,1 (1,8)

0,5 (0,9)

7,4 (3,6)

1,27 (1,14)

11,0 (1,7)

All speakers

5,7 (6,6)

3,7 (5,0)

2,5 (3,7)

6,4 (4,3)

0,9 (1,0)

10,8 (3,4)

4.2 Analysis of correlation of pauses and punctuation marks

The information on frequency of using punctuation in spoken language was obtained by analyzing the quantity of full stops and commas in transcriptions. Figure 3 shows meaning of the pauses in determining punctuation in speech. Among all full stops in transcription, 39 % are correlated with occurrences of a breath pause, 27 % a silent pause, and 20 % a filled pause (Fig 3a). Among all commas, 28 % are pointed by a silent pause, 20 % a breath pause, and 6 % a filled pause (Fig 3b). Lack of any kind of a pause (words bonding in pronunciation) was registered in 20 % occurrences of full stops and 46 % commas for spontaneous speech and only for 1.3 % full stops and 42 % commas for read speech (Fig. 4). Among all occurrences of filled pauses, 8 % indicate full stops and 6 % indicate commas; among breath pauses the proportions are, respectively, 10 and 11 % (Fig. 5).
Fig. 3

Different types of pauses determining a full stops and b commas in aspect of types of filled pauses signalizing punctuation [23]

Fig. 4

Different types of pauses determining full stops and commas [23]

Fig. 5

Proportions of filled pauses and breath pauses occurrences correlated with full stops or commas [23]

However, the usage of different types of pauses for signalization of punctuation is strongly individualized between speakers, as presented in Tab. 4. To facilitate the observation of inter-speaker differences, the intensity of connection between pauses and punctuation was graded with grayscale. Although the general tendency was signalization of full stops with breaths and lack of any kind of a pause in the place of comma, the variation between speakers is considerable.
Table 4

Percent of pauses events denoting full stops and commas for each speaker (P presentations/orations, T translation, R radio interviews)

 

Full stops

Commas

Recordings

n_p.

s_p.

f_p.

b_p.

n_p,

s_p,

f_p,

b_p,

P1_5

12

65

24

0

38

49

18

0

P2_5

4

23

73

0

40

45

15

0

P3_3

7

93

0

0

38

63

0

0

P4_1

18

18

0

64

67

17

0

17

P5_2

40

0

20

40

64

9

0

27

P6_1

0

13

13

75

70

20

0

10

P7_1

0

25

25

50

43

21

7

29

P8_1

0

14

29

57

67

17

0

17

P9_1

0

0

67

33

64

18

18

0

P10_1

25

25

25

25

48

43

5

5

P11_5

3

45

0

52

12

22

0

67

P12_4

33

25

3

40

56

24

1

18

Type P

14

34

20

32

44

34

7

16

T1_1

57

14

0

29

71

29

0

0

T2_1

25

25

25

25

57

0

14

29

T3_1

38

50

0

13

75

25

0

0

T4_1

0

45

0

55

38

13

0

50

T5_1

50

13

0

38

63

0

13

25

T6_5

19

13

16

53

51

23

2

25

T7_5

67

6

6

22

49

27

2

22

T8_5

0

25

4

71

28

28

4

40

T9_2

0

33

11

56

38

8

0

54

T10_4

30

19

15

37

61

18

2

18

T11_1

40

0

0

60

46

15

0

38

T12_2

18

12

0

71

32

16

16

37

Type T

25

20

8

47

49

20

4

27

R1_10

47

14

3

36

63

20

2

15

R2_10

12

11

25

53

45

13

18

24

R3_10

36

4

22

39

69

4

14

14

R4_10

30

11

32

27

63

5

9

23

R5_10

33

16

41

10

61

17

11

12

R6_10

44

0

43

14

73

12

8

8

Type R

34

9

17

40

62

12

13

13

All speakers

23 (20)

22 (21)

17 (19)

38 (22)

53 (15)

21 (14)

6 (7)

21 (17)

4.3 Differences between speakers in quality and quantity of pauses

Using feature vectors p s specific for each speaker s, we investigated correlation of each pair of speakers (formula 3). The obtained correlation matrix is presented in Fig. 6. Distribution of correlation of the given speaker with the others is illustrated in Fig. 7.
Fig. 6

a Correlation matrix for 30 speakers. b Cumulative distribution function and histogram of correlation coefficients for pairs of speakers

Fig. 7

Boxplot for correlation coefficients distribution for each speaker

As it is presented in Figs. 5 and 6, speakers vectors were usually correlated to a small extent or not correlated. Speakers’ distance from a mean vector was calculated according to formula (2). Distribution of results is presented in Fig. 8.
Fig. 8

Histogram of coefficients γ s for all speakers

Having annotated a large quantity of breaths and filled pauses “yyy” for every speaker (with the latter not appearing only for 2 out of 30 speakers in our corpus), we decided to analyze inter-speaker differences. We observed that the durations of breaths in our corpus have mean 392 ms, standard deviation 118 ms, median 368 ms, and quantiles are as follows: 0.25 is 312 ms and 0.75 is 455 ms. For the filled “yyy” pauses, the mean is 398 ms, standard deviation is 183 ms, median is 362 ms, and quantiles are as follows: 0.25 is 278 ms and 0.75 is 484 ms. Analysis of variance showed, that both for breaths (p = 7.6E−50) and for filled “yyy” pauses (p = 7.62E−22) the mean duration differences between speakers are statistically significant.

Figure 9a shows the results of ANOVA of breath duration of different speakers, and Fig. 9b shows the same for filled “yyy” pause duration. Analysis of those plots leads to the conclusion that although the differences are not statistically significant for every speaker pair, it makes sense to group the speakers into 2 or 3 categories, as in: speakers taking short breaths, speakers taking breaths of average length, speakers taking long breaths, and the same for “yyy” fillers. If more recordings of a single speaker were available, similar analysis could be made for the frequency of pause occurrence.
Fig. 9

Results of ANOVA analysis for 30 speakers: a Breath duration. b Filler “yyy” duration

For the parameters of breaths and fillers duration, Gaussian models were created (in Fig. 10, an example for nine speakers is presented). The preliminary analysis suggests that the features can be used for classification with Gaussian Mixture Models (GMM).
Fig. 10

Gaussian models fitted for nine speakers for a breath duration and b filler “yyy” duration

An interesting observation is that 8 (out of 30) of the speakers in our corpus do not use “mmm” filled pauses at all, while most of them do it rarely (on average about 1 per minute, compared to almost 7 filled “yyy” pauses per minute). The most frequent use of them is made by speaker T9_2 at about 3.5 “mmm” pause per minute. This leads to a conclusion that frequent usage of “mmm” filled pause is a characteristic feature of a speaker.

In the next experiment, standardized values of each feature were quantized into three values: low, medium and high, in reference to the distribution of each feature. For the quantized feature matrix (Fig. 11a), a clusterization was performed, using Euclidean distance measure. The obtained dendrograms with a heatmap representation allow to easily observe in which features the most similar speakers were alike.
Fig. 11

a Features matrix with quantized values. b Heatmap with dendrograms for speakers and features

4.4 Group analysis

To investigate the influence of experience and oratorical abilities on pauses and speech rate, we divided a corpus of spontaneous monologues into recordings of experienced speakers (professors and politicians) and inexperienced speakers (mainly students). Average values of selected temporal features of each group are compared in Table 5.
Table 5

Comparison of selected features for experienced and inexperienced speakers: average values and standard deviation (in brackets)

Parameter

Professional speakers

Inexperienced speakers

#words/minute

108 (23)

117 (26)

#words/sentence

17 (4)

22 (6)

#f_p/minute

4 (4)

10 (5)

n_p. [%]

12 (15)

13 (13)

s_p. [%]

26 (31)

24 (23)

f_p. [%]

10 (12)

34 (30)

b_p. [%]

50 (17)

27 (8)

As expected intuitively, professionals speak more slowly, with less disfluencies and formulate shorter sentences, which makes their speech more adjusted for efficient listening and understanding by recipients. Also their dynamic breathing rhythms are much more concordant with sentences boundaries (a half of full stops were correlated with breath pauses). Such conscious dynamic breathing (taking a breath before beginning of a sentence or phrase) is one of the basic voice emission principles, often emphasized by authors of handbooks on speaking skills and techniques.

The comparison of presentations, translations and radio dialogues can be observed in Fig. 12. Some of differences are significant and can be interpreted in accordance to intuitive situational context conditions. In radio interviews speakers tend to speak much faster, which is conditioned by the determined time for the conversation. Surprisingly, their filled pauses are much longer than for the rest of analyzed recordings.
Fig. 12

Differences in distribution of each feature between 3 groups of speakers (P presentations/orations, T translation, R radio transmissions)

4.5 Using pauses for speaker recognition—evaluation results

For the experiment, recordings from audiobooks were used (50 speakers, 15 min for each speaker). The choice was made in order to obtain a regular set of long enough recordings in the same situational context (reading a story) from a similar group of speakers (professional lectors). Entire set was used to train UBM and Total Variability matrix in the i-vector system.

Recordings were split into train part of 5 min and four test parts of 2.5 min each. It allowed performing 1200 cross-validation tests: 200 verification of authorized users (target trials) and 1000 simulation of impostors attacks (10 impostors with 2 random recordings were chosen randomly for each speaker). Parameters of silent pauses (f1–f21) were automatically extracted with VAD algorithm and the feature sets were processed as independent stream (as presented in Fig. 1) according to the methodology described in chapter 3.4.

The results of speaker verification task of a baseline i-vector system (using only MFCC features) are presented in Fig. 13, where the red and blue histograms are normalized distributions of target and impostor scores respectively. Vertical axis refers to FNR and FPR values. This result was obtained for the system based on 1024-component GMM-UBM and 400-dimensional total variability subspace. In this case EER reached the smallest value—3 %. The result is considered as sound for the number of impostor and target speakers used in the test. The performance of the system might be enhanced by gender-dependent approach or incorporation of score processing like PLDA or score normalization techniques, but the goal of this experiment was measuring efficacy of a simple baseline speaker verification system on the dataset.
Fig. 13

Performance of the baseline system

Performance of the system based on pauses features is illustrated in Fig 14. For the evaluation three features were used: durations of the silent pauses, number of silent pauses per minute (f1) and ratio between sum of pause duration (f2) to entire signal length. Due to limited number of data points that was extracted from test samples it was necessary to use minimal number of components in GMM to prevent overfitting. Best results were obtained with 4-component GMM. Such configuration resulted in EER equal to 40 %. Overlapped distributions in Fig 14 suggest that used features are little discriminative in speaker comparison. This meager result implies that applied features, without any further processing, should not be used as a standalone input into GMM classifier in a speaker discrimination task.
Fig. 14

Performance of system based on pause-related features only

Figure 15 presents DET plots for Pauses and i-vectors systems and for the fusion of the systems performed with a Bosaris Toolkit, where 20 % of the result scores were used to train fusion algorithm. Value of 20 % were chosen empirically, as an optimal point for DET plot. Modification of this value by enlarging training dataset did not change the positions of the curve but reduced its resolution due to lower number of test points. The fusion of the scores of the two systems caused no gain in overall performance and reveals reduction of efficacy by 1 % in terms of EER.
Fig. 15

Detection Error Trade-off plot for pause-based and MFCC-based systems and their fusion.

4.6 Automatic recognition of type of spontaneous speech

For this task, as our features we used a time series of silent pause information extracted in an online manner, where each point indicates an appearance of a silent pause. Three features were used in the experiment: the duration of the silent pause instance, s_p per minute (f1) and percentage of s_p time in recording (f2), where f1 and f2 were calculated online, based on the silent pause instances gathered up to this point. To perform classification, we adopted a diagonal covariance GMM classifier with three mixtures (one for each class of spontaneous speech). The best result was achieved for three Gaussian components in each mixture. Experiment was carried out using a “leave-one-out” cross-validation scheme.

The classifier achieved 78 % accuracy. Table 6 shows the Precision, Recall and F1 score achieved by the classifier in this task. As illustrated by the confusion matrix in Fig. 16, the worst performing class is the T type (translations), as it tends to be mistaken as a P type (presentation/oration). We suspect this is due to speakers making long silent pauses in both scenarios—to make a rhetoric effect in case of P, and because of the necessity to wait for more context before translating an utterance in case of T.
Table 6

Results of automatic recognition of types of spontaneous speech

Type

Precision

Recall

F1-score

Support

P

0.71

0.91

0.80

11

T

0.78

0.70

0.74

10

R

1.00

0.67

0.80

6

Average/ total

0.80

0.78

0.78

27

Fig. 16

Confusion matrix for automatic recognition of type of spontaneous speech

4.7 Automatic classification of read and spontaneous speech

In this task, we confronted our spontaneous speech recordings (P, T, and R classes) with 50 audiobook recordings (A class). In the distinction between read and spontaneous speech, seven features (f1–f7) describing silent pauses were used. This choice was due to presence of this type of pauses in any recording (in contrast to filled pauses which are absent in read speech and breath pauses which are present only in good quality recordings) and easiness of detecting them automatically. The silent pauses were found automatically using VAD and then, for each recording, a vector of seven features was calculated. Logarithmization and normalization of the parameters improved the results.

The best accuracy in this task was obtained using XGBoost classifier (Table 7). However, it should be noted that the dataset is imbalanced in terms of classes, and this classifier exhibits bias toward the read speech class which shows as increasing read speech recall and decreasing spontaneous speech recall in regard to less complex classifiers such as the decision tree. Nonetheless, all classifiers perform better than if classification was done by chance (50 % accuracy), or by always indicating the class with higher count (65 % accuracy).
Table 7

Results of automatic recognition of read and spontaneous speech - comparison of classifiers

Classifier

Type

Precision

Recall

F1 score

Accuracy

Decision tree

Read

0.75

0.78

0.76

0.69

Spontaneous

0.56

0.52

0.54

Average

0.68

0.69

0.69

Logistic regression

Read

0.74

0.86

0.80

0.71

Spontaneous

0.63

0.44

0.52

Average

0.70

0.71

0.70

SVM (RBF kernel)

Read

0.75

0.88

0.81

0.73

Spontaneous

0.67

0.44

0.53

Average

0.72

0.73

0.71

Random forest

Read

0.73

0.86

0.79

0.70

Spontaneous

0.61

0.41

0.49

Average

0.69

0.70

0.68

XGBoost

Read

0.75

0.94

0.83

0.75

Spontaneous

0.79

0.41

0.54

Average

0.76

0.75

0.73

We observed that read speech was better recognizable than spontaneous speech (see Fig. 17), which we believe is partially a result of classification bias, but also a result of the higher diversity of spontaneous speech class examples.
Fig. 17

Confusion matrix for automatic recognition of read/spontaneous speech

5 Discussion

Majority of speaker recognition systems do not include suprasegmentals. High-level features of speech signal, like pauses, although statistically they were proved to be speaker-specific, are dependent also on other factors, like situational context, stress level, kind of linguistic task. This weakens their possible usage for speaker recognition. It should also be remembered that obtaining data on pauses requires much longer (than e.g. standard MFCC analysis) segment of continuous speech to perform analysis, which is not a desirable situation in a text-dependent system which operate on short utterances (e.g. one sentence). Relatively large observation period (at least one minute) sufficient for acquiring information on pausing style, constraints applying the approach only to certain sort of biometric systems (text-independent systems or forensic applications).

The results obtained from testing a system based on pauses only (40 %) are similar to EER achieved in Peskin et al. test (36.1 to 43.3 % EER) using different pause-related features alone [16]. However, more types of models and classifiers should be tested in future works. Probably pauses would be better included into model using HMM chain, as applied in [25] or as a part of n-gram models [26].

This data is not enough in itself to perform biometric verification or identification of a speaker; however, it can be used to enhance speech technology applications by including additional information in speaker’s profile, such as the following: speaker breathes frequently, takes short breaths, makes filled pauses infrequently, etc.

In the second experiment, it was showed that the distribution and structure of pauses in speech, represented by three parameters, are specific for the type of speech and sufficient to automatically classify them with 78 % accuracy. We showed that parameters such as a number of each pause type occurrences per minute or statistics of pause duration bring important information about speaker’s habits. Advantage of the approach is simplicity, low computational complexity and robust feature extraction. Breath events [2] and filled pauses [23] can be automatically detected in a speech signal. It allows including the features easily in speech technology systems.

The obtained knowledge on pauses meaning can be merged with analysis of other temporal features (phoneme length, energy, fundamental frequency) in order to build algorithms for punctuation detection in speech. Since lack of punctuation and occurrence of disfluencies in spontaneous speech transcripts are factors that disturb their processing by natural language processing systems, parsers or information extraction systems, automatic analysis of pauses can help to make spontaneous speech transcripts more readable for both human and NLP systems.

Finally, in the biomedical field, the research on pauses is meaningful in affect detection. All analyzed kinds of pauses carry information on speaker current emotional state. Frequency and regularity of pausing behavior, based on obtained models, is currently tested in the task of automatic emotion recognition. It can lead directly to include it in systems for monitoring mental illnesses, since quantity and duration of silent pauses can be indicators of emotional state of the speaker or a measurable symptom of psychic disorders like schizophrenia or bipolar affective disorders. Measuring breath frequency in acoustic signal can be a cheap and easily available method for estimation of physical effort level, measure of physical fitness or diagnostics of potential respiratory dysfunctions (e.g. sleep apnea).

6 Conclusions

In this paper, we deliver the numerical description of pauses in Polish speech. Three types of acoustic pauses (silence, breaths and fillers), two types of punctuation marks (full stops and commas), and co-occurrences of acoustic and syntactic pauses were proved to be speaker dependent. Pausing behavior was investigated in several contexts (spontaneous speech during presentation, simultaneous interpretation, interview and read speech - reading a novel).

Connotations between pauses and punctuation, as well as frequency and types of pauses vary between individuals and depend on speaking style of each person, speech quality, culture, experience and preparation for oral presentations. Thereby, the temporal features can possibly be used as a valuable source of paralinguistic information. However, even though our results were better than similar previous studies, the differences were not sufficient to differentiate speakers. Verification of the hypothesis that they improve speaker recognition system was negative for scenario of modeling pauses with UBM and GMM models. Another modeling methods will be evaluated in future works.

An attempt to automatically recognize three types of spontaneous speech resulted in 78 % accuracy and distinguishing read and spontaneous speech with 75 % accuracy, using pause-related features only. This result shows usefulness of pauses to distinguish between different situational context and cognitive task and therefore it could find application for automatic discourse analysis and conversation modeling purposes. Presented statistical models of pauses will be a fundament for studying usefulness of the information in different applications, like ASR or emotion recognition systems. Further research will cover also other reasons of pauses frequency and duration variability (a type of personality of the speaker and emotional arousal). Feature vector dimensionality will be reduced. Analyses will be also conducted on more regular sets of recordings, e.g., the same speaker in different situational contexts.

Declarations

Acknowledgements

The project was supported by The Polish National Centre for Research and Development granted by decision 072/R/ID1/2013/03 and the National Science Centre support allocated on the basis of a decision DEC-2011/03/D/ST6/00914. This work was supported by the AGH University of Science and Technology under Deans Grant 15.11.230.252 and Statutory works 11.11.230.017.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Department of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology
(2)
Techmo

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Copyright

© The Author(s). 2016