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首页> 外文会议>Annual conference of the International Speech Communication Association;INTERSPEECH 2010 >A Feature Extraction Method for Automatic Speech Recognition Based on the Cochlear Nucleus
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A Feature Extraction Method for Automatic Speech Recognition Based on the Cochlear Nucleus

机译：基于耳蜗核的语音自动识别特征提取方法

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Motivated by the human auditory system, a feature extraction method for automatic speech recognition (ASR) based on the differential processing strategy of the AVCN, PVCN and the DCN of the cochlear nucleus is proposed. The method utilizes a zero-crossing with peak amplitudes (ZCPA) auditory model as synchrony detector to discriminate the low frequency for-mants. It utilizes the mean rate information in the synapse processing to capture the very rapidly changing dynamic nature of speech. Additionally, a temporal companding method is utilized for spectral enhancement through two-tone suppression. We propose to separate synchrony detection from synap-tic processing as observed in the parallel processing methodology in the cochlear nucleus. HMM recognition using isolated digits showed improved recognition rates in clean and in non-stationary noise conditions than the existing auditory model.
机译：在人类听觉系统的推动下，提出了一种基于AVCN，PVCN和耳蜗神经元DCN的差分处理策略的自动语音识别（ASR）特征提取方法。该方法利用零振幅峰交叉（ZCPA）听觉模型作为同步检测器来区分低频共振频率。它在突触处理中利用平均速率信息来捕获非常迅速变化的语音动态特性。另外，将时间压扩方法用于通过两音抑制的频谱增强。我们建议将同步检测从突触处理中分离出来，如在耳蜗核的并行处理方法中所观察到的。与现有的听觉模型相比，使用孤立数字进行的HMM识别在干净和非平稳的噪声条件下显示出更高的识别率。

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