# A sub-band-based feature reconstruction approach for robust speaker recognition

- Furong Yan
^{1}Email author, - Yanbin Zhang
^{1}and - Jiachang Yan
^{1}

**2014**:40

https://doi.org/10.1186/s13636-014-0040-7

© Yan et al.; licensee Springer. 2014

**Received: **26 April 2014

**Accepted: **2 October 2014

**Published: **21 October 2014

## Abstract

Although the field of automatic speaker or speech recognition has been extensively studied over the past decades, the lack of robustness has remained a major challenge. The missing data technique (MDT) is a promising approach. However, its performance depends on the correlation across frequency bands. This paper presents a new reconstruction method for feature enhancement based on the trait. In this paper, the degree of concentration across frequency bands is measured with principal component analysis (PCA). Through theoretical analysis and experimental results, it is found that the correlation of the feature vector extracted from the sub-band (SB) is much stronger than the ones extracted from the full-band (FB). Thus, rather than dealing with the spectral features as a whole, this paper splits full-band into sub-bands and then individually reconstructs spectral features extracted from each SB based on MDT. At the end, those constructed features from all sub-bands will be recombined to yield the conventional mel-frequency cepstral coefficient (MFCC) for recognition experiments. The 2-sub-band reconstruction approach is evaluated in speaker recognition system. The results show that the proposed approach outperforms full-band reconstruction in terms of recognition performance in all noise conditions. Finally, we particularly discuss the optimal selection of frequency division ways for the recognition task. When FB is divided into much more sub-bands, some of the correlations across frequency channels are lost. Consequently, efficient division ways need to be investigated to perform further recognition performance.

## Keywords

## 1 Introduction

The performance of speaker or speech recognition systems degrades rapidly when they operate under conditions that differ from those used for training. Therefore, accomplishing noise robustness is a key issue to make these systems deployable in real world conditions. Solutions have been presented to solve this issue, such as feature-based [1]–[3], score-based [4],[5], model-based [6]–[8], i-vectors [9], and the missing data technique (MDT) [10]–[12].

MDT can compensate for disturbances of the arbitrated type, so that this method which is based on the time-frequency representation is suitable to the problem of noise mismatch [12].

In MDT, two different methods have been considered to perform speech or speaker recognition with incomplete data: marginalization [13]–[15] and reconstruction [16],[17]. In marginalization, the unreliable components are discarded or integrated up to the observed values. While the reconstruction method involves the estimation of the corrupted features using statistical methods, such as minimum mean square error (MMSE) [10], maximum *a posteriori* (MAP), and maximum likelihood (ML). Marginalization [11],[14] and reconstruction [10] have been applied in speaker recognition system. However, marginalization suffers from two main drawbacks [17],[18]. First, as known to us, utterance-level processing, such as mean and variance normalization, is capable of improving the recognition performance, but it cannot be performed with an incomplete spectrum [18]. Second, recognition has been carried out with spectral features. However, it is well known that cepstral features outperform spectral ones. Moreover, of all the methods, marginalization is assumed to have the most overhead. Consequently, if the complete reconstructed spectrogram is available, the recognizer is no longer constrained to perform recognition using spectral features. A more optimal set of parameters from the reconstructed spectrum will be derived.

In this paper, MAP reconstruction method [10] is used. Its efficiency significantly depends on the correlation between the spectral features. Conventional MAP reconstruction method is conducted on full-band [18],[19]. According to our analysis, the spectral vectors extracted from the sub-band have more relevance than the ones extracted from the full-band. The conclusion will be illustrated in Section 1. Based on the above theory and the sub-band idea [20]–[22], a multi-sub-band reconstruction approach is proposed to improve on the recognition performance. The principle is to divide the full-band into multiple sub-bands and then independently reconstruct missing features extracted from every sub-band. After that, those features from all sub-bands will be recombined to yield the typical mel-frequency cepstral coefficient (MFCC) vector.

As one of many feature enhancement methods, the proposed reconstruction approach can be used in speaker and speech recognition system. To evaluate its validity, this paper will combine the new reconstruction method with speaker recognition system.

This paper is organized as follows. In the next section, the theory of the proposed reconstruction approach is analyzed. Section 1 is devoted to describing the proposed reconstruction approach. Section 1 describes the baseline experiment system and the experimental framework which is adopted to evaluate the proposed technique. Finally, Section 1 concludes this paper and discusses some future directions.

## 2 The analysis of concentration

As we know, the more concentrated the feature vector is, the higher its redundancy is, that is, the greater its correlation is [23]. It is measured by the degree of concentration with principal component analysis (PCA).

*P*-dimensional mel log-spectral vector is used for reconstruction. Mel filters are used to represent a frame spectrum as a log-spectral vector of

*P*-dimensional (termed as full-band feature vector). The frequency region (0,

*f*

_{ s }/2) is divided into

*C*sub-bands. Let

*P*

_{ i }denote the number of mel filters corresponding to the

*i*th sub-band. Apparently,

*t*th frame and

*i*th sub-band, the output of mel filters (termed as the

*i*th sub-band feature vector) is represented as follows:

In order to analyze the degree of concentration of the feature vector ${\stackrel{\u20d7}{Y}}_{i}^{t}$, the eigenvalues of associated covariance matrix *Θ*_{
i
} need to be calculated and then need to be arranged in descending order. It is represented as $\left[{\lambda}_{i,1},{\lambda}_{i,2},\xb7\xb7\xb7{\lambda}_{i,{P}_{i}}\right]$.

*i*th sub-band feature vector ${\stackrel{\u20d7}{Y}}_{i}^{t}$ is in the space of the

*P*

_{ i }-dimension, the so-called concentration level ${M}_{R}^{i}\left(r\right)$ is introduced and computed as follows:

That is, *R*_{
i
}(*m*) is the accumulative contribution rate of the first *m* principle components. Concentration level ${M}_{R}^{i}\left(r\right)$ is the minimum *m* that makes *R*_{
i
}(*m*)>*r*, where *r* is a predefined concentration coefficient.

For certain *r*, a smaller ${M}_{R}^{i}\left(r\right)$ implies that the *i* th sub-band feature vector is confined along a smaller number of principle directions, and therefore, the feature vector is much more closely related to each other according to the above definition.

In the same manner, the degree of concentration of the full-band feature vector could be analyzed.

*m*principle components corresponding to the 4-sub-band and full-band is shown in Figure 1. The conclusion should be clear. The concentration level corresponding to each sub-band in the 4-sub-band is smaller than the one corresponding to the full-band.

*r*is 0.9, the concentration level which is corresponding to the data shown in Figure 2a,b is ${M}_{R}^{\left(\mathit{\text{high}}\right)}\left(r\right)=1$ and ${M}_{R}^{\left(\mathit{\text{low}}\right)}\left(r\right)=2$, respectively.

Considering the recorded positions of the 2-dimensional feature vector in Figure 2 and the corresponding contribution rate, together with our analysis, the following conclusion is obtained: the higher the redundancy of the data is, that is, the greater its correlation is, the smaller the corresponding concentration level is. As MAP reconstruction method is based on the correlation between the feature vectors, the smaller the concentration level is, the higher the validity of the reconstruction is.

## 3 Multi-sub-band reconstruction for speaker recognition system

As one of many feature enhancement methods, the multi-sub-band reconstruction method in MDT can be applied in the Gaussian mixture model (GMM) [24], the SVM-GMM [25], and the universal background model (UBM)-GMM recognition system. Based on the validity of the UBM-GMM system shown in [11], the proposed reconstruction method is evaluated in a UBM-GMM speaker recognition system. In this section, the MDT-based speaker recognition system is described.

### 3.1 UBM-GMM model

In this paper, a speaker-independent UBM is used. A speaker-dependent model can be derived from UBM by adapting the UBM parameters to the speech material of the corresponding speaker using MAP estimation [11],[26].

### 3.2 Feature vector

Mel log-spectral vector and MFCC are used in the reconstruction and recognition stage, respectively. The unreliable components are reconstructed based on the statistical relationship between the log-spectral vector.

### 3.3 Mask estimation

*m*(

*t*,

*k*) is determined by estimating the local SNR in individual T-F units. SNR-based mask estimation method is applied to decide whether a T-F unit is reliable.

where${\left|\hat{\stackrel{\u20d7}{S}}(t,k)\right|}^{2}$ and ${\left|\hat{\stackrel{\u20d7}{N}}(t,k)\right|}^{2}$ represent the *k* th frequency bands of the power spectrum of speech and noise, respectively, in individual T-F units. What calls for special attention is that the estimation of speech and noise components is carried out in the spectral domain before applying mel filter.

The estimate of the noise spectrum is derived from the noisy signal spectrum. The estimation method is shown in [31]. The estimate of the speech spectrum ${\left|\hat{\stackrel{\u20d7}{S}}(t,k)\right|}^{2}$ can be derived by subtracting the estimated noise spectrum ${\left|\hat{\stackrel{\u20d7}{N}}(t,k)\right|}^{2}$ from the corrupted signal spectrum. In this paper, the technique to accomplish this is to perform spectral subtraction by applying an SNR-dependent gain function MMSE log-STSA [32] in the frequency domain.

### 3.4 MAP estimation for unreliable components

*μ*and covariance matrix

*Θ*. According to the nature of Gaussian distribution, $p\left({\stackrel{\u20d7}{x}}_{r};\stackrel{\u20d7}{\mu},\Theta \right)$ and $p\left({\stackrel{\u20d7}{x}}_{u};\stackrel{\u20d7}{\mu},\Theta \right)$ would therefore also be Gaussian [33]. Consequently,

where *Θ*_{
ru
} is the cross covariance between${\stackrel{\u20d7}{x}}_{r}$ and ${\stackrel{\u20d7}{x}}_{u}$ and ${\Theta}_{\mathit{\text{ru}}}={\Theta}_{\mathit{\text{ur}}}^{T}$.

*C*is a normalizing constant. The following equation can be obtained from Equations 11, 12, and 13.

*Θ*

_{ rr }must be learned from the training corpus. A vector is said to belong to the cluster that is most likely to have generated it. As the distribution of the vector is assumed to be Gaussian, the cluster membership ${\hat{m}}_{\stackrel{\u20d7}{x}\left(t\right)}$ of a vector $\stackrel{\u20d7}{x}\left(t\right)$ is defined as

and then the unreliable components of the vector are reconstructed using MAP estimation method.

### 3.5 The proposed multi-sub-band reconstruction approach

*P*mel filter to smooth the

*N*FFT magnitude coefficients. The reconstruction is individually conducted on 2 sub-bands consisting of consecutive channels (

*P*/2-dimensional channels) with no band overlap (sub-band 1: channel 1 to

*P*/2, sub-band 2: channel

*P*/2+1 to

*P*). The reconstruction method falls neatly into two parts as shown in Figure 5. In the first part, the statistical parameters (SP) used in construction are individually trained for different sub-bands. The steps of the second part are as follows:

- (a)
The estimation of speech and noise components is carried out in the spectral domain.

- (b)
A mask will be obtained which classifies the T-F representation into reliable and unreliable components corresponding to the frequency range of

*P*mel filters. The above two steps are carried out before applying the mel filter. - (c)
*P*mel filters are used to smooth the power spectrum and then its logarithm is taken. - (d)
The mel log-spectral vector is multiplied by the mask estimated in step (b).

- (e)
The feature vector corresponding to full-band is divided into ones corresponding to 2 sub-bands.

- (f)
Based on SP trained in the first part, the feature vectors corresponding to every sub-band are reconstructed, individually.

- (g)
The reconstructed vector of 2-sub-band is recombined to yield the typical MFCC vector.

### 3.6 Baseline system

The system described in [11] assumes that the unreliable components are bounded between zero and the observed mel log-spectrum and the mel log-spectrum is independent, and marginalization is applied to process the corrupted vector. The feature vector used in recognition is a *P*-dimensional mel log-spectrum. We compare the performance of the proposed system with the baseline system.

## 4 Experiments

New reconstruction method is evaluated on a closed set of 30 speakers and 140 utterances per speaker. The sampling frequency is 16 KHz. For each speaker, 70% of the available speech material is randomly selected to train the corresponding speaker model, 7% is used for training SP for reconstruction stage, and the remaining 23% is used for test.

In the training stage, we use a voice activity detector (VAD) based on power to ensure that silence frames would not impact on the establishing model.

Speaker recognition performance is evaluated on a subset of ten randomly selected speakers involving a total of 30 sentences per speaker (20 sentences for training speaker-dependent GMM and 10 sentences for testing). In the test phase, utterances are mixed at various SNRs with noise signals drawn from the NOISEX database [34].

### 4.1 Experiment 1: performance comparison between marginalization and reconstruction including full-band and 2-sub-band reconstruction

In the first experiment, we compare the performances of two systems which use the marginalization and reconstruction methods to process the corrupted features and then evaluate the validity of the proposed reconstruction method. The point is that recognition has to be carried out with spectral features in the former system. While in the latter system, MFCC are extracted for recognition.

**Recognition performance of FB, 2-SB reconstruction, and marginalization in the presence of different types of noise (unit: %)**

0 dB | 5 dB | 10 dB | 15 dB | 20 dB | ||
---|---|---|---|---|---|---|

Babble | FB | 82.60 | 84.02 | 87.01 | 87.49 | 89.49 |

2-SB | 82.96 | 85.30 | 87.84 | 89.30 | 91.25 | |

Marginalization | 63.71 | 64.88 | 66.44 | 67.69 | 69.75 | |

Factory1 | FB | 76.52 | 82.10 | 87.40 | 88.00 | 88.33 |

2-SB | 80.01 | 82.25 | 87.70 | 89.13 | 90.23 | |

Marginalization | 67.54 | 68.10 | 68.33 | 68.51 | 69.40 | |

Pink | FB | 75.12 | 80.78 | 84.83 | 87.55 | 89.11 |

2-SB | 76.66 | 82.43 | 87.40 | 89.62 | 90.27 | |

Marginalization | 67.79 | 68.54 | 69.00 | 69.06 | 69.91 | |

White | FB | 77.10 | 83.02 | 84.40 | 87.16 | 89.71 |

2-SB | 78.81 | 83.60 | 86.09 | 88.70 | 89.82 | |

Marginalization | 68.00 | 68.92 | 70.21 | 70.77 | 71.21 | |

Destroyer-engine | FB | 76.51 | 82.81 | 86.43 | 86.29 | 88.47 |

2-SB | 82.16 | 86.60 | 86.52 | 87.92 | 88.68 | |

Marginalization | 66.64 | 67.26 | 68.30 | 68.48 | 69.97 | |

Average | FB | 77.57 | 82.55 | 86.01 | 87.30 | 89.02 |

2-SB | 80.12 | 84.04 | 87.11 | 88.93 | 90.05 | |

Marginalization | 66.74 | 67.54 | 68.46 | 68.90 | 70.05 |

- (a)
It can be observed in Table 1 that the performance obtained from both reconstruction methods clearly outperforms the baseline system.

- (b)
The results show that 2-sub-band reconstruction method performs better than full-band for all noise types. The recognition performance is higher at a larger SNR.

- (c)
The recognition performance in babble noise is higher than the other four noise types in most cases for two kinds of reconstruction methods.

- (d)
The corresponding relative improvements regarding full-band reconstruction are 2.55%, 1.49%, 1.10%, 1.63%, and 1.03% at a SNR of 0, 5, 10, 15, and 20 dB, respectively. Recognition performance improves the most at a SNR of 0 dB.

- (e)
The improved recognition performance is 6.04%, 6.97%, 8.99%, 5.63%, and 11.37% in babble, factory1, pink, white, and destroyer-engine noise, respectively. Recognition performance improves the most in destroyer-engine noise.

*r*is 0.95, the corresponding concentration levels are ${M}_{R}^{\mathit{\text{FB}}}\left(r\right)=10$, ${M}_{R}^{1}\left(r\right)=6$, and ${M}_{R}^{2}\left(r\right)=5$. Based on the conclusion shown in Section 1, a smaller ${M}_{R}^{i}\left(r\right)$ implies a stronger concentration for the feature vector. Consequently, since the correlation of every sub-band is stronger than the full-band, the performance of the 2-sub-band reconstruction approach is better.

**The contribution rate (%) of every principle component**

FB | SB1 | SB2 | |
---|---|---|---|

1 | 50.161 | 69.455 | 66.811 |

2 | 22.291 | 13.475 | 17.025 |

3 | 6.935 | 3.292 | 6.812 |

4 | 5.236 | 2.315 | 3.084 |

5 | 3.424 | 2.315 | 2.051 |

6 | 1.945 | 2.139 | 1.345 |

7 | 1.708 | 1.514 | 0.951 |

8 | 1.218 | 1.185 | 0.715 |

9 | 1.125 | 0.898 | 0.490 |

10 | 1.002 | 0.651 | 0.327 |

11 | 0.869 | 0.445 | 0.233 |

12 | 0.698 | 0.240 | 0.157 |

### 4.2 Experiment 2: influence of different division ways of full-band

**Different division ways of full-band and the corresponding recognition performance**

Channel | Sub-bands | Recognition (%) |
---|---|---|

number | ||

12 | 1-2, 3-4, 5-6, 7-8, 9-10, 11-12,13-14,15-16, | 74.18 |

17-18, 19-20, 21-22, 23-24 | ||

8 | 1-3, 4-6, 7-9, 10-12, 13-15, 16-18, 19-21, 22-24 | 78.15 |

6 | 1-4, 5-8, 9-12, 13-16, 17-20, 21-24 | 77.83 |

4 | 1-6, 7-12, 13-18, 19-24 | 84.86 |

3 | 1-8, 9-16, 17-24 | 81.78 |

2 | 1-12, 13-24 | 82.96 |

When the full-band is divided into 12 sub-bands, the recognition performance is inferior. The observation shows that the correlations between the feature vector are lost when the number of sub-bands is more numerous.

## 5 Conclusions

This paper presents a new feature enhancement method, which is evaluated in a UBM-GMM speaker recognition system. In the proposed method, the reconstruction is executed on a partial sub-band independently and then the reconstructed spectrum is recombined into a complete spectrum to yield the conventional MFCC for recognition. Compared to full-band reconstruction method, recognition performance obtained by the proposed reconstruction approach has been shown to be higher in five noise types. The experiment has also reflected that the recognition performance depends on the frequency division ways, thus the optimal division ways need to be developed.

The first experiment has revealed the following results. First, MFCC features outperform spectral ones for speaker recognition. Second, the recognition performance obtained by reconstruction is higher than marginalization. Third, the recognition performance obtained by the 2-sub-band reconstruction method is superior to the full-band reconstruction in five noise types and at all SNRs. The second experiment has shown that different frequency division ways could influence on the recognition performance.

In order to achieve further recognition performance improvements, on the one hand, an optimal frequency division way will be very important. On the other hand, analyzing the distribution property of various noise types and then accurately identifying destroyed components are also research hot spots. In the end, research on mask estimation algorithms is required to precisely separate reliable from unreliable components.

## Declarations

## Authors’ Affiliations

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