Frame decorrelation for noise-robust speech recognition

被引:2
|
作者
Jung, HY
Kim, DY
Un, CK
机构
[1] Communications Research Laboratory, Department of Electrical Engineering, Korea Adv. Inst. Sci. and Technol., Yusong-Gu, Taejon 305-701
关键词
speech recognition; signal processing;
D O I
10.1049/el:19960808
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
The authors propose a frame decorrelation method to cope with background noise in speech recognition. Since noise is modelled as a stationary perturbation in most cases, it is effective to reduce slow-varying components. One example of using this principle is the highpass scheme. The proposed method has the same property as the highpass scheme. It transforms feature vector sequences into decorrelated sequences and enhances transition regions. Simulation results show that this method is effective for speech with significant noise, and works better than other highpass methods.
引用
收藏
页码:1163 / 1164
页数:2
相关论文
共 50 条
  • [31] Modeling sub-band correlation for noise-robust speech recognition
    McAuley, J
    Ming, J
    Hanna, P
    Stewart, D
    2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL I, PROCEEDINGS: SPEECH PROCESSING, 2004, : 1017 - 1020
  • [32] Noise-robust speech recognition by discriminative adaptation in parallel model combination
    Chung, YJ
    ELECTRONICS LETTERS, 2000, 36 (04) : 370 - 371
  • [33] A Noise-Robust Speech Recognition System Based on Wavelet Neural Network
    Wang, Yiping
    Zhao, Zhefeng
    ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, PT III, 2011, 7004 : 392 - 397
  • [34] APPROXIMATED PARALLEL MODEL COMBINATION FOR EFFICIENT NOISE-ROBUST SPEECH RECOGNITION
    Sim, Khe Chai
    2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2013, : 7383 - 7387
  • [35] Noise-robust Attention Learning for End-to-End Speech Recognition
    Higuchi, Yosuke
    Tawara, Naohiro
    Ogawa, Atsunori
    Iwata, Tomoharu
    Kobayashi, Tetsunori
    Ogawa, Tetsuji
    28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020), 2021, : 311 - 315
  • [36] Noise-robust cellular phone speech recognition using CODEC-adapted speech and noise models
    Kato, T
    Naito, M
    Shimizu, T
    2002 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-IV, PROCEEDINGS, 2002, : 285 - 288
  • [37] Kernel Power Flow Orientation Coefficients for Noise-Robust Speech Recognition
    Gerazov, Branislav
    Ivanovski, Zoran
    IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2015, 23 (02) : 407 - 419
  • [38] Sparse coding of the modulation spectrum for noise-robust automatic speech recognition
    Ahmadi, Sara
    Ahadi, Seyed Mohammad
    Cranen, Bert
    Boves, Lou
    EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING, 2014, : 1 - 20
  • [39] Multi-condition training for noise-robust speech emotion recognition
    Chiba, Yuya
    Nose, Takashi
    Ito, Akinori
    ACOUSTICAL SCIENCE AND TECHNOLOGY, 2019, 40 (06) : 406 - 409
  • [40] A Noise-type and Level-dependent MPO-based Speech Enhancement Architecture with Variable Frame Analysis for Noise-robust Speech Recognition
    Mitra, Vikramjit
    Borgstrom, Bengt J.
    Espy-Wilson, Carol Y.
    Alwan, Abeer
    INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5, 2009, : 2731 - +