SNR features for automatic speech recognition

Abstract

When combined with cepstral normalisation techniques, the features normally used in Automatic Speech Recognition are based on Signal to Noise Ratio (SNR). We show that calculating SNR from the outset, rather than relying on cepstral normalisation to produce it, gives features with a number of practical and mathematical advantages over power-spectral based ones. In a detailed analysis, we derive Maximum Likelihood and Maximum a-Posteriori estimates for SNR based features, and show that they can outperform more conventional ones, especially when subsequently combined with cepstral variance normalisation. We further show anecdotal evidence that SNR based features lend themselves well to noise estimates based on low-energy envelope tracking.