The speech recognition system based on bark wavelet MFCC

被引：0
|
作者：
Zhang, Xue-ying [1 ]
Bai, Jing [1 ]
Liang, Wu-zhou [1 ]
机构：
[1] Taiyuan Univ Technol, Coll Informat Engn, Taiyuan 030024, Shanxi, Peoples R China
来源：
2006 8TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, VOLS 1-4 | 2006年
关键词：
bark wavelet; speech recognition; MFCC;
D O I：
暂无
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：
Bark wavelet is a new wavelet which is especially designed for speech signal. Its base function satisfies time and bandwidth product least. Moreover, the Bark wavelet divides frequency band based on auditory model. This paper uses Bark wavelet in MFCC. It was used to make preprocessing before FFT. On the other hand, it was used to instead of DCT in MFCC for overcoming the DCT's disadvantage of fixed time-frequency resolution. Thus, a kind of good anti-noisy speech feature coefficient was obtained. Experimental results of speech recognition demonstrate that this new feature is more robust than the MFCC feature in noise environment and large vocabulary.
引用
收藏
页码：780 / +
页数：2
相关论文
共 50 条
  • [31] Chip design of MFCC extraction for speech recognition
    Wang, JC
    Wang, JF
    Weng, YS
    INTEGRATION-THE VLSI JOURNAL, 2002, 32 (1-2) : 111 - 131
  • [32] Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition
    Majeed, Sayf A.
    Husain, Hafizah
    Samad, Salina A.
    ARCHIVES OF ACOUSTICS, 2015, 40 (01) : 25 - 31
  • [33] A speech recognition approach with MFCC and fractal dimension
    Yao, Minghai
    Hu, Jing
    DCABES 2006 Proceedings, Vols 1 and 2, 2006, : 349 - 351
  • [34] Throat Microphone Speech Recognition using MFCC
    Vijayan, Amritha
    Mathai, Bipil Mary
    Valsalan, Karthik
    Johnson, Riyanka Raji
    Mathew, Lani Rachel
    Gopakumar, K.
    2017 INTERNATIONAL CONFERENCE ON NETWORKS & ADVANCES IN COMPUTATIONAL TECHNOLOGIES (NETACT), 2017, : 392 - 395
  • [35] Speech emotion recognition using MFCC-based entropy feature
    Mishra, Siba Prasad
    Warule, Pankaj
    Deb, Suman
    SIGNAL IMAGE AND VIDEO PROCESSING, 2024, 18 (01) : 153 - 161
  • [36] A MFCC-based CELP speech coder for server-based speech recognition in network environments
    Yoon, Jae Sam
    Lee, Gil Ho
    Kim, Hong Kook
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2007, E90A (03) : 626 - 632
  • [37] Automatic Recognition System for Dysarthric Speech Based on MFCC's, PNCC's, JITTER and SHIMMER Coefficients
    Zaidi, Brahim-Fares
    Boudraa, Malika
    Selouani, Sid-Ahmed
    Addou, Djamel
    Yakoub, Mohammed Sidi
    ADVANCES IN COMPUTER VISION, VOL 2, 2020, 944 : 500 - 510
  • [38] Emotion Recognition in Speech Using MFCC and Classifiers
    Ajitha, G.
    Prashanth, Addagatla
    Radhika, Chelle
    Chaitanya, Kancharapu
    COMPUTATIONAL VISION AND BIO-INSPIRED COMPUTING ( ICCVBIC 2021), 2022, 1420 : 197 - 207
  • [39] An efficient MFCC extraction method in speech recognition
    Han, Wei
    Chan, Cheong-Fat
    Choy, Chiu-Sing
    Pun, Kong-Pang
    2006 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-11, PROCEEDINGS, 2006, : 145 - +
  • [40] On the importance of components of the MFCC in speech and speaker recognition
    Zhen, B.
    Wu, X.
    Liu, Z.
    Chi, H.
    Beijing Daxue Xuebao Ziran Kexue Ban/Acta Scientiarum uaturalium Universitatis Pekinensis, 2001, 37 (03): : 371 - 378
12345