Human factor cepstral coefficients

Abstract

Automatic speech recognition (ASR) is an emerging field with the goal of creating a more natural man/machine interface. The single largest obstacle to widespread use of ASR technology is robustness to noise. Since human speech recognition greatly outperforms current ASR systems in noisy environments, ASR systems seek to improve noise robustness by drawing on biological inspiration. Most ASR front ends employ mel frequency cepstral coefficients (mfcc) which is a filter bank-based algorithm whose filters are spaced on a linear-log frequency scale. Although center frequency is based on a perceptually motivated frequency scale, filter bandwidth is set by filter spacing and not through biological motivation. The coupling of filter bandwidth to other filter bank parameters (frequency range, number of filters) has led to variations of the original algorithm with different filter bandwidths. In this work, a novel extension to mfcc is introduced which decouples filter bandwidth from the rest of the filter bank parameters by employing the relationship between filter center frequency and critical bandwidth of the human auditory system. The new algorithm, called human factor cepstral coefficients (hfcc), is shown to outperform the original mfcc and two popular variations in several ASR experiments and noise sources.