Noise Robust Speech Features for Automatic Continuous Speech Recognition using Running Spectrum Analysis

被引:2
|
作者
Ohnuki, Kazunaga [1 ]
Takahashi, Wataru [1 ]
Yoshizawa, Shingo [1 ]
Miyanaga, Yoshikazu [1 ]
机构
[1] Hokkaido Univ, Grad Sch Informat Sci & Technol, Sapporo, Hokkaido 0600814, Japan
关键词
D O I
10.1109/ISCIT.2008.4700172
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this report, new robust speech feature is introduced and applied for an automatic continuous speech recognition system. Using these features, the noise robust continuous speech recognition can be realized. The new running spectrum analysis (RSA) method is used in order to remove un-speech components over 15 Hz in modulation spectrum domain. Using RSA, speech features are emphasized for the design of tri-phone HMM where the tri-phone HMM is used in continuous speech recognition. In order to show the performance of the developed system, some comparisons with conventional one are given in experiments.
引用
收藏
页码:150 / 153
页数:4
相关论文
共 50 条
  • [41] Noise robust speech recognition using subband-crosscorrelation analysis
    Kajita, S
    Takeda, K
    Itakura, F
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 1998, E81D (10) : 1079 - 1086
  • [42] Speech Enhancement for Automatic Speech Recognition Using Complex Gaussian Mixture Priors for Noise and Speech
    Astudillo, Ramon F.
    Hoffmann, Eugen
    Mandelartz, Philipp
    Orglmeister, Reinhold
    ADVANCES IN NONLINEAR SPEECH PROCESSING, 2010, 5933 : 60 - 67
  • [43] Topological invariants as speech features for automatic speech recognition
    Kacur, Juraj
    Chudy, Vladimir
    INTERNATIONAL JOURNAL OF SIGNAL AND IMAGING SYSTEMS ENGINEERING, 2014, 7 (04) : 235 - 244
  • [44] Normalizing the speech modulation spectrum for robust speech recognition
    Xiao, Xiong
    Chng, Eng Siong
    Li, Haizhou
    2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL IV, PTS 1-3, 2007, : 1021 - +
  • [45] New Features Using Robust MVDR Spectrum of Filtered Autocorrelation Sequence for Robust Speech Recognition
    Seyedin, Sanaz
    Ahadi, Seyed Mohammad
    Gazor, Saeed
    SCIENTIFIC WORLD JOURNAL, 2013,
  • [46] Noise-robust automatic speech recognition using a predictive echo state network
    Skowronski, Mark D.
    Harris, John G.
    IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2007, 15 (05): : 1724 - 1730
  • [47] Noise-robust automatic speech recognition using a discriminative echo state network
    Skowronski, Mark D.
    Harris, John G.
    2007 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-11, 2007, : 1771 - 1774
  • [48] Multiple resolution analysis for robust automatic speech recognition
    Gemello, R
    Mana, F
    Albesano, D
    De Mori, R
    COMPUTER SPEECH AND LANGUAGE, 2006, 20 (01): : 2 - 21
  • [49] Model compensation using robust features for robust speech recognition
    Zhang, Jun
    Wei, Gang
    Shuju Caiji Yu Chuli/Journal of Data Acquisition and Processing, 2003, 18 (03):
  • [50] Robust speech recognition using a noise rejection approach
    Khan, E
    Levinson, R
    IEEE INTERNATIONAL JOINT SYMPOSIA ON INTELLIGENCE AND SYSTEMS - PROCEEDINGS, 1998, : 326 - 335