Automatic speech recognition and speech variability: A review
Speech Communication
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We consider the performance of speech recognition in noise and focus on its sensitivity to the acoustic feature set. In particular, we examine the perceived information reduction imposed on a speech signal using a feature extraction method commonly used for automatic speech recognition. We observe that the human recognition rates on noisy digit strings drop considerably as the speech signal undergoes the typical loss of phase and loss of frequency resolution. Steps are taken to ensure that human subjects are constrained in ways similar to that of an automatic recognizer. The high correlation between the performance of the human listeners and that of our connected digit recognizer leads us to some interesting conclusions, including that typical cepstral processing is insufficient to support speech information in noise.