A comparison of two strategies for ASR in additive noise: missing data and spectral subtraction

A Comparison of Two Strategies for ASR in Additive Noise: Missing Data and Spectral Substraction

Missing data theory, spectral subtraction and signal-to-noise estimation for robust ASR: an integrated study

In the missing data approach to robust Automatic Speech Recognition (ASR), time-frequency regions which carry reliable speech information are identified. Recognition is then based on these regions alone. In this paper, we address the problem of identifying reliable regions and propose two criteria to solve this based on negative energy ( $ s < 0 ) and SNR ( $ s s n 2 2 2 < + ). These criteria a...

Probabilistic compensation of unreliable feature components for robust speech recognition

Missing feature theory is well studied in robust ASR context, many works have been done on additive noise of different colors. These are based mainly on classical spectral subtraction and marginal density techniques. This paper addresses the problem of temporal distortion of feature components, that is all about time domain instead of frequency one. No specific noise model and extract computati...

Techniques for handling convolutional distortion with 'missing data' automatic speech recognition

In this study we describe two techniques for handling convolutional distortion with ‘missing data’ speech recognition using spectral features. The missing data approach to automatic speech recognition (ASR) is motivated by a model of human speech perception, and involves the modification of a hidden Markov model (HMM) classifier to deal with missing or unreliable features. Although the missing ...

Feature extraction combining spectral noise reduction and cepstral histogram equalization for robust ASR

This work is mainly focused on showing experimental results using a combination of two methods for noise compensation which are shown to be complementary: classical spectral subtraction algorithm and histogram equalization. While spectral subtraction is focused on the reduction of the additive noise in the spectral domain, histogram equalization is applied in the cepstral domain to compensate t...