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

Furong Yan,Jiachang Yan,Yanbin Zhang

doi:10.1186/s13636-014-0040-7

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.

Highlights

The performance of speaker or speech recognition systems degrades rapidly when they operate under conditions that differ from those used for training
5 Conclusions This paper presents a new feature enhancement method, which is evaluated in a universal background model (UBM)-Gaussian mixture model (GMM) speaker recognition
The reconstruction is executed on a partial sub-band independently and the reconstructed spectrum is recombined into a complete spectrum to yield the conventional mel-frequency cepstral coefficient (MFCC) for recognition

Summary

Introduction

The performance of speaker or speech recognition systems degrades rapidly when they operate under conditions that differ from those used for training. In MDT, two different methods have been considered to perform speech or speaker recognition with incomplete data: marginalization [13,14,15] and reconstruction [16,17]. As one of many feature enhancement methods, the proposed reconstruction approach can be used in speaker and speech recognition system. Considering the recorded positions of the 2dimensional 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. The correlation between the feature vectors, the smaller the concentration level is, the higher the validity of the reconstruction is

Multi-sub-band reconstruction for speaker recognition system

Mask estimation

The proposed multi-sub-band reconstruction approach

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

Findings

Conclusions