> >The paper presents a feature extraction method, named as Normalized Gammachirp Cepstral Coefficients (NGCC) that incorporates the properties of the peripheral auditory system to improve robustness in noisy speech recognition. The proposed method is based on a second order low-pass filter and normalized gammachirp filterbank to emulate the mechanisms performed in the outer/middle ear and cochlea. The speech recognition performance of this method is conducted on the speech signals in real-world noisy environments. Experimental results demonstrate that method outperformed the classical feature extraction methods in terms of speech recognition rate. The used Hidden Markov Models based speech recognition system is employed on the HTK 3.4.1 platform (Hidden Markov Model Toolkit).
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机译： > >本文提出了一种特征提取方法，称为归一化Gammachirp倒谱系数（NGCC），该方法结合了外围听觉系统的属性，以提高嘈杂语音识别中的鲁棒性。所提出的方法基于二阶低通滤波器和归一化的gammachirp滤波器组，以模拟在外/中耳和耳蜗中执行的机制。该方法的语音识别性能是在现实环境中对语音信号进行的。实验结果表明，该方法在语音识别率方面优于经典特征提取方法。在HTK 3.4.1平台（Hidden Markov Model Toolkit）上采用了基于隐马尔可夫模型的语音识别系统。
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