Filterbank Analysis of MFCC Feature Extraction in Robust Children Speech Recognition

被引：0

作者：

Naing, Hay Mar Soe ^{[1
]}

Miyanaga, Yoshikazu ^{[2
]}

Hidayat, Risanuri ^{[1
]}

Winduratna, Bondhan ^{[1
]}

机构：

[1] Gadjah Mada Univ, Dept Elect Engn & Informat Technol, Yogyakarta 55281, Indonesia

[2] Hokkaido Univ, GS Informat Sci & Techonol, GI CoRE GSB, Sapporo, Hokkaido 0600814, Japan

来源：

2019 INTERNATIONAL SYMPOSIUM ON MULTIMEDIA AND COMMUNICATION TECHNOLOGY (ISMAC) | 2019年

关键词：

children speech recognition; gammatone frequency integration; MFCC; speaker adaptative model;

D O I：

10.1109/ismac.2019.8836181

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper focused on the issue of robustness in children speech recognition system. The shape of filterbank analysis is presented in this study to suppress the additive background noise in acoustic features of children speakers. In addition, the Linear Discriminant Analysis (LDA), the Maximum Likelihood Linear Transform (MLLT) and feature space Maximum Likelihood Linear Regression (fMLLR) features are applied to build the speaker adaptive acoustic model with the help of Kaldi speech recognition toolkit. The performance of Gammatone filterbank and Bark-scale filterbank based Cepstral features were evaluated under contaminated situations using five different types of noise at a range of signal to noise ratio (SNR) 10dB to -10dB. As the detailed analysis shown, the performance of Gammatone frequency integration is superior to Mel Frequency Cepstral Coefficient (MFCC) in different types of additive background noise and various SNR situations.

引用

页数：6

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