Robust speech recognition using wavelet coefficient features
Robust speech recognition using wavelet coefficient features
复制标题
使用小波系数特征的鲁棒语音识别
DOI：
10.1109/asru.2001.1034680
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
Anna C. Gilbert
中科院分区：
文献类型：
--
作者：
Maya R. Gupta;Anna C. Gilbert
我们提出了一种新的特征向量，用于使用去噪小波系数的鲁棒语音识别；对意外附加噪声或频谱失真的更强鲁棒性始于更鲁棒的声学特性。小波系数的使用源于人类声学过程建模以及小波系数捕获重要时间和频率特征的能力。小波去噪强调了有关语音信号的最显着信息并增加了鲁棒性。我们在 TIMIT 数据库、其对应的 NTIMIT 数据库和低通滤波器失真的小规模实验中使用去噪余弦包特征，展示了令人鼓舞的结果。
We propose a new vein of feature vectors for robust speech recognition that use denoised wavelet coefficients; greater robustness to unexpected additive noise or spectrum distortions begins with more robust acoustic features. The use of wavelet coefficients is motivated by human acoustic process modelling and by the ability of wavelet coefficients to capture important time and frequency features. Wavelet denoising accentuates the most salient information about the speech signal and adds robustness. We show encouraging results using denoised cosine packet features on small-scale experiments with the TIMIT database, its NTIMIT counterpart, and low-pass filter distortions.