Histogram Equalization Utilizing Window-Based Smoothed CDF Estimation for Feature Compensation

スポンサーリンク

概要

• 論文の詳細を見る

• In this letter, we propose a new histogram equalization method to compensate for acoustic mismatches mainly caused by corruption of additive noise and channel distortion in speech recognition. The proposed method employs an improved test cumulative distribution function (CDF) by more accurately smoothing the conventional order statistics-based test CDF with the use of window functions for robust feature compensation. Experiments on the AURORA 2 framework confirmed that the proposed method is effective in compensating speech recognition features by reducing the averaged relative error by 13.12% over the order statistics-based conventional histogram equalization method and by 58.02% over the mel-cepstral-based features for the three test sets.

• （社）電子情報通信学会の論文
• 2008-08-01

著者

• Kim Hoirin

  School Of Engineering At Information And Communications University

• SUH Youngjoo

  School of Engineering at Information and Communications University

• Suh Youngjoo

  School Of Engineering Information And Communications University

• KIM Munchurl

  School of Engineering, Information and Communications University

• Kim Munchurl

  School Of Engineering Information And Communications University

関連論文

• Noise Robust Speaker Identification Using Sub-Band Weighting in Multi-Band Approach(Speech and Hearing)
• Text-Independent Speaker Identification in a Distant-Talking Multi-Microphone Environment(Speech and Hearing)
• Response Time Reduction of Speech Recognizers Using Single Gaussians(Speech and Hearing)
• Histogram Equalization Utilizing Window-Based Smoothed CDF Estimation for Feature Compensation
• Soft Counting Poisson Mixture Model-Based Polling Method for Speech/Nonspeech Classification(Speech and Hearing)
• Noise Robust Speaker Identification Using Sub-Band Weighting in Multi-Band Approach

スポンサーリンク