Classification-based spoken text selection for LVCSR language modeling
© The Author(s) 2017
Received: 22 May 2017
Accepted: 5 October 2017
Published: 17 October 2017
Large vocabulary continuous speech recognition (LVCSR) has naturally been demanded for transcribing daily conversations, while developing spoken text data to train LVCSR is costly and time-consuming. In this paper, we propose a classification-based method to automatically select social media data for constructing a spoken-style language model in LVCSR. Three classification techniques, SVM, CRF, and LSTM, trained by words and parts-of-speech are comparatively experimented to identify the degree of spoken style in each social media sentence. Spoken-style utterances are chosen by incremental greedy selection based on the score of the SVM or the CRF classifier or the output classified as “spoken” by the LSTM classifier. With the proposed method, just 51.8, 91.6, and 79.9% of the utterances in a Twitter text collection are marked as spoken utterances by the SVM, CRF, and LSTM classifiers, respectively. A baseline language model is then improved by interpolating with the one trained by these selected utterances. The proposed model is evaluated on two Thai LVCSR tasks: social media conversations and a speech-to-speech translation application. Experimental results show that all the three classification-based data selection methods clearly help reducing the overall spoken test set perplexities. Regarding the LVCSR word error rate (WER), they achieve 3.38, 3.44, and 3.39% WER reduction, respectively, over the baseline language model, and 1.07, 0.23, and 0.38% WER reduction, respectively, over the conventional perplexity-based text selection approach.
Large vocabulary continuous speech recognition (LVCSR) systems now play an increasingly significant role in daily life. Many commercial applications of LVCSR are widely employed, e.g., medical dictation, getting weather information, data entry, speech transcription, speech-to-speech translation, railway reservation, etc. However, in some systems, e.g., a speech-to-speech translation and interactive voice response (IVR) for customer service, speech input is highly conversational while it is more of a written style in medical dictation. A spoken language and a written language are different in several aspects including the word choice and the sentence structure. Hence, it is important to consider the language style for creating an efficient language model (LM) for a LVCSR system.
Typical speech recognition uses a LM to introduce linguistic restrictions that helps the recognizer figure out a word sequence. In general, a LM is built by using a text corpus, and its performance depends on the data size and text quality. For creating LVCSR systems in different domains and styles, it is necessary to find appropriate text sources well matched to the task domain as well as the style of speech input. A straightforward way to create a conversational text corpus is to transcribe recorded human conversations. However, transcribing is much costly and time-consuming, and thus it is quite difficult to get a large amount of conversational data to reliably train a LM. Much effort has been devoted to the unsupervised and semi-supervised acoustic model training [1–3] to exploit the untranscribed data. Most of these works focus on generating better quality hypothesis and on improving confidence measure for better data selection. In other direction, acquiring large text from the Internet is a popular way nowadays. Filtering text appropriate for the targeted LM is also an important step towards effective use of these data. Word perplexity was used as a similarity criterion to select text for a target domain [4, 5]. Another approach based on comparing the entropy between domain-specific and general domain LMs was proposed [6, 7]. The relative entropy-based criterion in  was also chosen for building topic-specific LMs. They showed that these techniques could produce a better domain-specific LM than that by random data selection.
This paper targets on building Thai LVCSR serving general daily conversations. To enlarge the LM training data suitable for this task, acquiring text, and filtering for spoken-style text are needed. Twitter, a well-known social media microblog, is attractive as the text length up to 140 characters tends to make the language more informal and sometimes produces incomplete sentences, which are one characteristic of the spoken language. Using the Twitter text, called tweets, to build LM was first introduced in , where the in-vocabulary hit rate was used as a criterion to select useful tweets. In addition to the classical entropy-based sentence selection methods previously proposed, in this paper, modern machine learning algorithms including support vector machines (SVM), conditional random fields (CRF), and long short-term memory neural network (LSTM) are comparatively investigated to improve the precision of spoken language sentence selection. With training data representing highly written language such as newspaper and highly spoken language such as telephone conversation, the trained model is expected to estimate the degree of spoken style of an input text. This model is suitable for selecting spoken sentences from mixed-style data such as tweets. The selected tweets are finally used to construct a spoken-style LM which is interpolated to a baseline LM to improve the overall Thai LVCSR performance. The resulted LM is comparatively evaluated with a LM trained by a set of exact spoken text, in terms of both the perplexity and the LVCSR word error rate (WER) on two different tasks.
This paper is organized as follows: we first briefly introduce the characteristics of spoken and written languages and also describe existing Thai large vocabulary speech corpora in Section 2. We present our process for collecting data from Twitter in Section 3. In Section 4, the proposed method of style-based data selection for constructing spoken LM is explained. In Section 5, we describe the experiments to evaluate the proposed style classifier in terms of the classification accuracy, the perplexity of the LM, and the LVCSR recognition performance. We finally conclude our work and discuss our future direction in Section 6.
2 Thai spoken and written languages
2.1 Distinction between difference text styles
Characteristics of Thai spoken language vs. written language
(1) A sentence is complete.
(2) A sentence is less sophisticated: fewer subordinate clauses .
(2) A sentence is more sophisticated: more subordinate clauses .
(3) A sentence starts with a topic-comment structure .
(3) A sentence starts with a subject-predicate form .
(4) Repetition, word duplication or paraphrasing, often appears .
(4) A sentence contains less repetition .
(5) A filler, a word or expression which is filled up when a speaker is in the process of thinking, often appears .
(5) A filler does not appear .
(6) A final particle, e.g. /khâʔ/, /khráp/, /nî:aʔ/, and /c-â:ʔ/, often appears .
(6) A sentence contains fewer final particles .
(7) Slang and foreign words are often used.
(7) Formal lexicon is used.
2.2 Thai Corpora for analysis of spoken and written styles
The amount of texts in Thai large vocabulary speech corpora
LOTUS is a Large vOcabulary Thai continUous Speech corpus specially designed for developing Thai LVCSR. Utterances in the LOTUS corpus were recorded in a reading style where the reading prompts were taken from a Thai text corpus, ORCHID . This corpus contains articles from various sources, such as magazines, Thai encyclopedia, and journals, and thus can be considered as a general-domain written-style corpus. The LOTUS corpus contains 55 h of speech from 48 speakers.
LOTUS-Cell 2.0, or LOTUS-Cell for short in the context of this paper, is a large Thai telephone speech corpus. It contains three parts of speech data, answers to closed-ended questions, answers to open-ended questions and dialog speech. The questions and discussion topics were designed to elicit speech data which have their contents conformed to the domains of potential automatic speech recognition (ASR) applications such as transportation, tourism, and health care. The corpus contains recorded speech from 212 speakers with gender balance. The amount of recorded speech is 90 h.
LOTUS-SOC is a Large vOcabulary Thai ContinUous Speech SOCial media corpus which aims at reducing the effort in creating a conversational speech corpus so that a larger corpus could be collected. The LOTUS-SOC corpus is created by recording Thai native speakers uttering Twitter messages through a mobile application. By using this data collection method, we can avoid the process of segmenting the recorded speech into utterances and also the need to transcribe the data. The corpus contains recorded speech from 208 speakers and approximately 172 h of speech. The age range of speakers is 11–58 years old.
VoiceTra4U-M (VT) is a speech translation application in sport and travel domains developed under the Universal Speech Translation Advanced Research (U-STAR) consortium (http://www.ustarconsortium.com/qws/slot/u50227/index.html). This consortium, consisting of 26 research institutes from 23 countries, aims to enable people in the world to communicate without any language barriers. The application allows five people to chat simultaneously in 23 different languages including Thai. It is available on the iOS platform, but the consortium also plans to make it available on Android platform due to a rapid growth of Android smartphones in many countries. The corpus contains speech from various Thai speakers and obtained approximately 22 h of speech recorded on mobile devices in real environments. The speech data were manually transcribed.
The detail of each speech corpora set used in this study
Number of utterances
Training the classification model
Evaluation the classifier performance
Training the classification model
Evaluation the classifier performance
Optimization the selection of confidence groups
Evaluation the recognition performance
Evaluation the recognition performance
3 Twitter data collection
Twitter data are used as a data source for performing style-based data selection to build our spoken LM. In this study, we collected approximately 2 million Thai tweets during February to March 2013 via the available Twitter REST API. This API allows us to specify desired keywords when acquiring the data. We will refer to this text collection as a Thai Twitter text corpus throughout the rest of this paper. 150,000 tweets were randomly selected as initial data for examining in our experiments.
Like Chinese, and Japanese, Thai is an unsegmented script language, i.e., there is no boundary marker between words while boundary markers on phrase and sentence levels are often ambiguous. Furthermore, there is no capital letter to indicate the beginning of a new sentence or a proper noun. Therefore, after the text is cleaned and normalized, we need to identify word boundaries. In this work, we use TLex , a Thai word segmentation tool based on CRF, to automatically identify word boundaries in tweet texts.
4 Style-based data selection
The goal of this work is to retrieve spoken-style text from social media data, Twitter, for building an appropriate LM in spoken-style LVCSR. Since Twitter data may also contain written-style text such as formal news tweets from news agencies, we first explore the use of text categorization to classify text into two categories: spoken and written. After that, the selected spoken-style sentences and the existing spoken text, CELL-TRN, are used to train the spoken LM. Finally, an interpolated LM between prepared baseline and spoken LMs is used as the final LM for LVCSR. An overall diagram, baseline LM construction, classification features, methods, and data selection are discussed in Sections 4.1, 4.2, 4.3, 4.4, and 4.5, respectively.
4.1 Overall diagram
In the case of SVM or CRF, sentences are separated into groups according to classification scores.
where LM baseline is the baseline LM trained by the existing data as described in Section 4.2, LM spoken is the LM trained by the selected spoken-style sentences combined with the CELL-TRN, and λ is the weighting factor for tuning the final model.
4.2 Baseline LM
Text resources for language model training
Number of utterance
Number of token
HIT-BTEC is a Thai translated version of the multilingual Olympic-domain corpus developed by Harbin institute of technology (HIT)  and the Basic Travel Expression Corpus (BTEC) . This corpus was initially created for speech-to-speech translation research. The HIT corpus includes utterances in five domains related to Olympic games, namely, traveling, dining, sports, traffic, and business. The Thai version of BTEC and HIT was constructed under the Universal Speech Translation Advanced Research (USTAR) consortium. The total amount of data in the HIT-BTEC corpus is nearly 160,000 utterances.
BEST  is a Thai text corpus developed under the BEST (Benchmark for Enhancing the Standard of Thai language processing) project. Articles in BEST were collected from eight different genres: academic article, encyclopedia, novel, news, Buddhism, law, lecture, and Wikipedia. These articles were manually segmented into words by linguists. The total amount of available data is approximately 7 million words.
Web-blog is a large collection of Thai web text from chatting blog and discussion forum on a famous website in Thailand. The corpus consists of articles from eight different genres: mobile, travel, camera, films, residence, news, automobile, and woman’s life. This corpus was collected from June 2011 to March 2012. Tlex  was used to automatically identify word boundaries. The total amount of data in this corpus is nearly 140,000 utterances.
LOTUS-BN  is a Thai television broadcast news corpus which includes audio recordings of hour-long news programs and their transcriptions. Each news program is segmented into small parts, i.e., sections and utterances. There are 18 news topics in the corpus, for instance, politics, sport, and weather. The corpus contains approximately 156 h of speech from 43 female speakers and 38 male speakers. Data in LOTUS-BN are divided into 3 sets: a training set (TR), a development test set (DT), and an evaluation test set (ET). There is no overlapping speaker among the TR, DT and ET sets. Only the TR set (around 50,000 utterances) was used to train the LM.
4.3 Classification features
According to the difference between spoken and written languages summarized in Table 1, we consider using words in the input text and their parts of speech (POSs) as features for style classification. The word feature represents different word choices between the spoken and written language. For instance, informal words are more likely to occur in a spoken-style utterance while formal words are more likely to occur in a written-style utterance. Similarly, some POSs, e.g., particle and personal pronoun, are more likely to occur in spoken language while some POSs, e.g., conjunction which indicates a complex sentence, are more likely to occur in the written language. To automatically tag the POS of each word, we constructed a POS tagger using CRF. We trained the CRF model with manually tagged data which contain 3 million words taken from the BEST corpus . Articles in BEST were manually segmented into words and then POS tagged by linguists. The tagset consists of 35 POSs  which was modified from the 47-POS tagset used in the ORCHID corpus . We use the word and its contexts, i.e. the previous word and the following word, as features for predicting the POS of each word. Our CRF-based POS tagger has 97.6% accuracy.
4.4 Stylistic text classification methods
In this work, three machine learning approaches, SVM, CRF, and LSTM are compared for style classification. SVM is one of the most effective machine learning algorithms for many complex binary classification problems. One remarkable property of SVM is its ability to learn regardless of the dimensionality of the feature space. Given a category, each example belongs to one of two classes referred to as the positive and negative class.
For the kernal function in this study, we investigate two basic kernels, Linear and Radial Basis Function (RBF).
An input vector of the SVM classifier contains 40,964 elements representing all lexical words and all POSs. Each element value is the frequency of the word or POS in the input sentence. We train the SVM to predict two classes, a positive class (+) for a spoken text utterance and a negative class (−) for a written one. Given an input utterance, the SVM outputs a real value of the decision function, the larger value the output of SVM is, the more spoken the utterance becomes. It is noted that the SVM output score used in this study is the predicted value that can be used to order the test utterance for ranking the spoken-like degree.
CRF is undirected graphical model for segmenting and labeling structured data . The CRF model represents a conditional distribution p(y|x) and dependencies over the observation sequence x. We assume that X=(x 1,x 1,…,x T ) is a input sequence and Y=(y 1,y 2,…,y T ) is a set of label sequence.
We train a CRF by maximizing the log-likelihood of a label sequence in the training data. Our CRF classifier works at word level.
Each word is labeled as “spoken” in spoken training utterances or “written” in written ones. We also use words and POSs as classification features. The current word, previous word and the following word along with their POSs are used to predict a classification label, “spoken” or “written,” for each word. The output of CRF for each input sentence is a conditional probability; a high probability reflects “spoken” and a low reflects “written.”
LSTM is an alternative architecture for recurrent neural network inspired by the human memory systems. The LSTM is a model that allows us to input a sequence of inputs. At every step, the model will update its internal representation of the input so far, giving it a form of memory.
Using the LSTM classifier in this work, both words and their POSs are also employed as classification features at an utterance level. We implemented the LSTM with a single hidden layer, 0.01 of learning rate for weight updates, and a stochastic gradient-based optimization algorithm  for weight optimization. Each training sentence is labeled as “1” for a spoken class and “2” for a written class. Since we are attempting to classify the whole sentence, not an individual word, we only consider the last output of the network as the actual classification result. The LSTM output contains class elements, a class “1” for a spoken-style and a class “2” for a written-style.
4.5 Data selection methods for building spoken LM
After classifying, the utterances are organized into groups based on the scores from SVM or CRF, or the output classes from LSTM. From SVM and CRF classification process, the output score is a real value.
The organized groups of SVM- and CRF-based scoring calculated from Twitter text data
Score ≥ 0.9
−1.0≥ score >−2.0
−2.0≥ score >−3.0
−3.0≥ score >−4.0
Using the LSTM classifier, the output contains class elements, the class “1” for a spoken text utterance and the class “2” for a written one. Consequently, in this work, the spoken LM are trained from the union of the existing spoken-style corpus (CELL-TRN) and the selected sentences with the class “1.” With this technique, the total number of utterances for building spoken LM are 151K.
We evaluate the proposed style classifier in terms of the classification accuracy, the perplexity of the result LM and the recognition performance. Experimental results are discussed in the following sub-sections.
5.1 Experimental conditions
Acoustic model training data of our LVCSR composes of 773 h of speech from LOTUS , LOTUS-BN , LOTUS-SOC , VoiceTra4U-M, and other unpublished sources. VoiceTra4U-M is a speech translation application in sport and travel domains developed under the Universal Speech Translation Advanced Research (U-STAR) project (http://www.ustar-consortium.com/). Twenty-two h of speech were recorded on mobile devices in the real environment. We used the Kaldi Speech Recognition Toolkit  to first train a conventional GMM-based acoustic model. We then applied the Maximum Mutual Information (MMI) discriminative training technique described in . Each frame of speech data was converted into a sequence of 39 dimensional feature vectors of 12 MFCCs appended with a log energy, and their first and second derivatives. We used a 25-ms frame length with 10-ms window shift. Features from a context window of 3 frames to the left and right were also included. A Linear Discriminate Analysis (LDA) was also applied to the feature space to reduce feature dimensions to 40.
(1) ALL: All sentences containing both “written” and “spoken.”
(2) Random: A limited number of sentences randomly selected.
(3) PPL: Sentences selected by the perplexity-based method .
(4) SVM: Sentences selected by the SVM classifier. With this technique, 98K sentences are selected as spoken-style utterances.
(5) CRF: Sentences selected by the CRF classifier. In this case, 174K sentences are selected.
(6) LSTM: Sentences selected by the LSTM classifier. 151K sentences are chosen as spoken-style text.
As each selection techniques produces different numbers of utterances, for fair comparison, the number of sentences from Random and PPL cases are varied at 98K, 151K, and 174K as in SVM, CRF, and LSTM techniques, respectively.
For building each spoken LM, the CELL-TRN and the selected sentences from each method were adopted as described in Section 4.1. In the recognition step, the final LM is interpolated by these spoken LM to the baseline LM.
We evaluated our approaches in two different recognition tasks: Twitter posting (SOC) and VoiceTra4U-M speech-to-speech translation (VT). The evaluation data set is classified as either spoken or written utterances. It contained 4000 utterances from 5 speakers in the office environment taken from the LOTUS-SOC and 1916 utterances from the VT-TST of the VoiceTra4U-M mobile application.
5.2 Classification accuracy
To train a style classifier, we used the LOTUS corpus as a representation of written-style utterances and the LOTUS-Cell as a representation of spoken-style utterances. Every utterance in the LOTUS corpus was labeled as “written” while every utterance in the LOTUS-Cell corpus was marked as “spoken.” Four thousand three hundred thirty utterances in the LOTUS-TRN set and 40,000 utterances in the Cell-TRN set, described in Section 2.2, were used to train classification models while 557 utterances in the LOTUS-DEV set and 15,475 utterances in the Cell-DEV set were used to evaluate the classifier performance.
The classification performance of style classifiers evaluated on LOTUS-DEV and Cell-DEV sets
F score (%)
You can see that LSTM classifier has the lowest precision value but its recall is the highest. The high value of recall indicates that LSTM classified the spoken utterance almost completely. However, many written sentences were also falsely classified as we can see from the low value of precision. On the other hand, we achieve the lowest recall and highest precision when using the CRF classifier. This indicates that a few written sentences were mixed selected, but not all the spoken utterances were selected. The results also demonstrated that both of the classification F scores and accuracies, the results of SVM, CRF, and LSTM classifiers are comparable. Therefore, in this work, we chose to compare multiple classification models, since we are interested to see how each classifier does in predicting the spoken degrees that are assigned to the utterances. For SVM, the RBF kernel has a slightly better performance than the linear one. Therefore, the RBF kernelwas used to select spoken utterances for LM training in the latter experiment.
5.3 Language model perplexity
In this experiment, the performance of the style classifier was evaluated in terms of LM perplexity with respect to a known spoken-utterance test set (VT-DEV). 51.84, 91.61, and 79.93% of the utterances in the corpus were classified as spoken utterances by SVM, CRF, and LSTM classifiers respectively. We can see that the number of picked utterances of each classifier seem to be a difference due to our organized groups, shown in Table 5, which affect on the degree of spoken.
A trigram LM was trained by the SRILM toolkit  with modified Kneser-Ney discounting. Three LMs trained from spoken utterances selected by CRF, LSTM, and SVM classifiers respectively were interpolated with baseline LM (Base) and then evaluated on the VT-DEV set of spoken data. For comparison, we also investigated the LMs made with other selection methods, ALL, Random, and PPL, as described in Section 5.1. In cases of Random and PPL, we randomly selected 174K, 151K, and 98K utterances to train LMs to make the size of the training data comparable to each proposed method, CRF, LSTM, and SVM, respectively. These LMs were also interpolated with Base and then evaluated on the same VT-DEV set.
Perplexities of language models trained from mixed-style and spoken-style utterances evaluated on VT-TST and SOC sets
5.4 Recognition performance
From the results, we can see that all proposed classification-based selection methods effectively decreased the WER from those by Base, ALL, Random, and PPL in all test cases. In case of the VT test set, it is obvious that the ALL and Random selection methods deteriorate the performance of LM. However, in the SOC test set, the recognition results of ALL and Random selection methods are quite better than that of the PPL. The recognition results of our proposed techniques are also slightly improved compared to others. This might be due to the fact that we use a development set from the VoiceTra4U-M data (VT-DEV) to tune obtain an interpolation weight. However, the evaluation results on the SOC set demonstrate that the VT-DEV can be used even if in the open dataset. Compared with the Base, when no social media sentences were used, the average improvement with the proposed CRF, LSTM, and SVM were 3.44, 3.39, and 3.38%, respectively. With the increase of all social media data (ALL), which contains both “written” and “spoken” utterances, the average WER improvement of the proposed CRF, LSTM, and SVM became 1.44, 1.40, and 1.39%, respectively. This shows the fact that using a large amount of social media data from the Internet, without style-based classification, gives no benefit. Moreover, the CRF, LSTM, and SVM approaches achieved a reduction of 0.23, 0.38, and 1.07% in average WER over a conventional perplexity-based approach, respectively. It can conclude that the proposed techniques can obviously improve the selection of spoken-style data and still achieve slightly better recognition accuracies.
In this paper, we explored the possibility of using data from social media such as Twitter to augment the lack of large text corpora for LVCSR language modeling. The problem of mixed-style text, written- and spoken-like, in tweets was handled through our data selection approaches to determine spoken-like sentences for building LM in LVCSR. Three particular classification techniques were investigated to identify spoken-style sentences in a Twitter corpus; SVM, CRF, and LSTM. We trained each style classifier using both words and parts-of-speech as input features. With style classification, we were able to classify the spoken sentences based on output scores, of SVM or CRF.
For LSTM, spoken sentences were directly determined by the classifier.
The selected spoken-style text were used to construct a spoken LM, which was then interpolated with the baseline LM to build a final LM for the LVCSR system.
Our experiments showed that the LM constructed by our proposed techniques was efficient for conversational LVCSR as shown by the reduced LM perplexity and WER. Compared with the use of all data in the Twitter corpus, trigram language models trained from tweets selected by CRF, LSTM, and SVM methods achieved up to 0.66, 4.29, and 5.56% absolute perplexity improvement and 1.44, 1.40, and 1.39% absolute WER improvement, respectively.
In summary, it was confirmed that the proposed approach efficiently improved the selection of spoken-like sentences, and improved the LVCSR performance on spoken-style tasks.
In the future, we plan to use more features such as syntactic features to improve style classification. Moreover, more advanced classification methods will be investigated. Active learning can also be conducted to refine classification labels assigned to each sentence in the corpus. Approaches focusing on transforming written to cope with speaking disfluencies such as inserting filled pause, repetition, repair, and false start, have been proposed [31–33]. This idea is attractive and could be added after the sentence selection process in order to increase the degree of spoken-style of the corpus.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This 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.
- E Egorova, JL Serrano, Semi-supervised training of language model on spanish conversational telephone speech data. Procedia Comput. Sci. 81:, 114–120 (2016).View ArticleGoogle Scholar
- S Novotney, R Schwartz, J Ma, in Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference On. Unsupervised acoustic and language model training with small amounts of labelled data (IEEE, 2009), pp. 4297–4300. https://scholar.google.co.th/scholar?hl=th&as_sdt=0%2C5&q=Unsupervised+acoustic+and+language+model+training+with+small+amounts+of+labelled+data&btnG=.
- K Yu, M Gales, L Wang, PC Woodland, Unsupervised training and directed manual transcription for lvcsr. Speech Commun. 52(7), 652–663 (2010).View ArticleGoogle Scholar
- J Gao, J Goodman, M Li, K-F Lee, Toward a unified approach to statistical language modeling for chinese. 1(1), 3–33 (2002). https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Toward+a+unified+approach+to+statistical+language+modeling+for+chinese&btnG=.
- T Misu, in Interspeech. Kawahara: A bootstrapping approach for developing language model of new spoken dialogue systems by selecting web text, (2006), pp. 9–13. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=A+bootstrapping+approach+for+developing+language+model+of+new+spoken+dialogue+systems+by+selecting+web+text&btnG=.
- RC Moore, W Lewis, in Proceedings of the ACL 2010 Conference Short Papers. Intelligent selection of language model training data, (2010), pp. 220–224. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Intelligent+selection+of+language+model+training+data&btnG=.
- A Axelrod, X He, J Gao, in Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP ’11. Domain adaptation via pseudo in-domain data selection, (2011), pp. 355–362. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Domain+adaptation+via+pseudo+in-domain+data+selection&btnG=.
- A Sethy, P Georgiou, SS Narayanan, in Proceedings of the Human Language Technologies (HLT) Conference. Selecting relevant text subsets from web-data for building topic specific language models (New York City, 2006), pp. 145–148. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Selecting+relevant+text+subsets+from+webdata+for+building+topic+specific+language+models&btnG=.
- A Jaech, M Ostendorf, Leveraging twitter for low-resource conversational speech language modeling. arXiv preprint arXiv:1504.02490 (2015). https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Leveraging+twitter+for+lowresource+conversational+speech+language+modeling&btnG=.
- A Prasithrathsint, Sociolinguistic Research on Thailand Languages. Language Sciences, (1998). (https://www.sciencedirect.com/science/article/pii/0388000188900174).
- A Chotimongkol, K Thangthai, C Wutiwiwatchai, in Co-ordination and Standardization of Speech Databases and Assessment Techniques (COCOSDA), 2014 17th Oriental Chapter of the International Committee for The. Utilizing social media data through similarity-based text normalization for lvcsr language modeling, (2014), pp. 1–6. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Utilizing+social+media+data+through+similaritybased+text+normalization+for+lvcsr+language+modeling&btnG=.
- S Kasuriya, V Sornlertlamvanich, P Cotsomrong, S Kanokphara, N Thatphithakkul, in Oriental COCOSDA. Thai speech corpus for Thai speech recognition, (2003), pp. 54–61. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Thai+speech+corpus+for+Thai+speech+recognition&btnG=.
- A Chotimongkol, N Thatphithakkul, S Purodakananda, C Wutiwiwatchai, P Chootrakool, C Hansakunbuntheung, A Suchato, P Boonpramuk, in Oriental COCOSDA Held Jointly with 2010 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE), 2010 International Conference. The development of a large thai telephone speech corpus: Lotus-cell 2.0, (2010). https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=The+development+of+a+large+thai+telephone+speech+corpus%3A+Lotus-cell+2.0&btnG=.
- A Chotimongkol, V Chunwijitra, S Thatphithakkul, N Kurpukdee, C Wutiwiwatchai, in Oriental COCOSDA Held Jointly with 2015 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE), 2015 International Conference. Elicit spoken-style data from social media through a style classifier, (2015), pp. 7–12. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Elicit+spokenstyle+data+from+social+media+through+a+style+classifier.&btnG=.
- P Chootrakool, V Chunwijitra, P Sertsi, S Kasuriya, C Wutiwiwatchai, in Oriental COCOSDA Held Jointly with 2016 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE), 2016 International Conference. Lotus-soc: A social media speech corpus for Thai lvcsr in noisy environments, (2016). https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Lotus-soc%3A+A+social+media+speech+corpus+for+Thai+lvcsr+in+noisy+environments.&btnG=.
- V Sornlertlamvanich, N Takahashi, H Isahara, in Proc. Oriental COCOSDA 1998. Thai part-of-speech tagged corpus: ORCHID, (1998), pp. 131–138. http://www.academia.edu/1215347/ORCHID_Thai_part-of-speech_tagged_corpus.
- C Haruechaiyasak, S Kongyoung, in in Proc. of SNLP. Tlex: Thai lexeme analyser based on the conditional random fields, (2009). https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Thai+lexeme+analyser+based+on+the+conditional+random+fields&btnG=.
- T Joachims, Advances in kernel methods, (1999). Chap. Making Large-scale Support Vector Machine Learning Practical. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Advances+in+kernel+methods+Joachims&btnG=.
- JD Lafferty, A McCallum, FCN Pereira, in Proceedings of the Eighteenth International Conference on Machine Learning, ICML ’01. Conditional random fields: Probabilistic models for segmenting and labeling sequence data, (2001), pp. 282–289. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Conditional+random+fields%3A+Probabilistic+models+for+segmenting+and+labeling+sequence+data.&btnG=.
- S Hochreiter, J Schmidhuber, Long short-term memory. Neural Comput.9(8), 1735–1780 (1997).View ArticleGoogle Scholar
- K Thangthai, A Chotimongkol, C Wutiwiwatchai, in INTERSPEECH. A hybrid language model for open-vocabulary Thai LVCSR, (2013), pp. 2207–2211. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=A+hybrid+language+model+for+open-vocabulary+Thai+LVCSR&btnG=.
- M Yang, H Jiang, T Zhao, S Li, in Chinese Spoken Language Processing: 5th International Symposium, ISCSLP 2006, Singapore, December 13-16, 2006. Proceedings. Construct trilingual parallel corpus on demand, (2006), pp. 760–767. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Construct+trilingual+parallel+corpus+on+demand&btnG=.
- G Kikui, E Sumita, T Takezawa, S Yamamoto, in INTERSPEECH. Creating corpora for speech-to-speech translation, (2003). https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Creating+corpora+for+speech-to-speech+translation&btnG=.
- K Kosawat, M Boriboon, P Chootrakool, A Chotimongkol, S Klaithin, S Kongyoung, K Kriengket, S Phaholphinyo, S Purodakananda, T Thanakulwarapas, C Wutiwiwatchai, in SNLP. BEST 2009: Thai word segmentation software contest, (2009), pp. 83–88. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=BEST+2009%3A+Thai+word+segmentation+software+contest&btnG=.
- A Chotimongkol, K Saykhum, P Chootrakool, N Thatphithakkul, C Wutiwiwatchai, in Oriental COCOSDA. LOTUS-BN: A Thai broadcast news corpus and its research applications, (2009), pp. 44–50. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=LOTUS-BN%3A+A+Thai+broadcast+news+corpus+and+its+research+applications&btnG=.
- P Boonkwan, Part-of-speech tagging guidelines for Thai. National Electronics and Computer Technology, 1–34 (2012).Google Scholar
- DP Kingma, J Ba, Adam: A method for stochastic optimization. CoRR. abs/1412.6980: (2014). https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=A+method+for+stochastic+optimization&btnG=.
- D Povey, A Ghoshal, G Boulianne, L Burget, O Glembek, N Goel, M Hannemann, P Motlicek, Y Qian, P Schwarz, J Silovsky, G Stemmer, K Vesely, in IEEE 2011 Workshop on Automatic Speech Recognition and Understanding. The Kaldi speech recognition toolkit, (2011). https://infoscience.epfl.ch/record/192584.
- L Bahl, P Brown, P de Souza, R Mercer, in Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP ’86, 11. Maximum mutual information estimation of hidden markov model parameters for speech recognition, (1986), pp. 49–52. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Maximum+mutual+information+estimation+of+hidden+markov+model+parameters+for+speech+recognition&btnG=.
- A Stolcke, in Proc. of the International Conference on Spoken Language Processing (ICSLP). SRILM - an extensible language modeling toolkit, (2002), pp. 901–904. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=SRILM+-+an+extensible+language+modeling+toolkit&btnG=.
- R Schwartz, L Nguyen, F Kubala, G Chou, G Zavaliagkos, J Makhoul, in Proceedings of the Workshop on Human Language Technology. On using written language training data for spoken language modeling, (1994), pp. 94–98. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=On+using+written+language+training+data+for+spoken+language+modeling&btnG=.
- Y Akita, T Kawahara, in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP ’07, vol. 4. Topic-independent speaking-style transformation of language model for spontaneous speech recognition, (2007), pp. 33–36. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Topicindependent+speakingstyle+transformation+of+language+model+for+spontaneous+speech+recognition.&btnG=.
- R Masumura, S Hahm, A Ito, in Interspeech. Training a language model using web data for large vocabulary japanese spontaneous speech recognition, (2011), pp. 1465–1468. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Training+a+language+model+using+web+data+for+large+vocabulary+japanese+spontaneous+speech+recognition.&btnG=.
- S Burusphat, Speech analysis: Nakhonpathom discourse analysis. Research Institute for Languages and Culture for rural development Mahidol University (1994). http://e-book.ram.edu/ebook/t/TH103/chapter11.pdf.
- S Chodchoey, in Proc. of the Second International Symposium on Language and Linguistics. Spoken and written discourse in thai: The difference, (1998). https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Spoken+and+written+discourse+in+thai%3A+The+difference&btnG=.