Towards Robust and Adaptive Speech Recognition Models towards Robust and Adaptive Speech Recognition Models
In this paper, we discuss a family of new Automatic Speech Rec ognition (ASR) approaches, which somewhat deviate from the usual ASR approaches but whi ch have recently been shown to be more robust to nonstationary noise, without requiring specific a daptation or “multi-style” training. More specifically, we will motivate and briefly describe new approaches b a ed on multi-stream and subband ASR. These approaches extend the standard hidden Markov model (HMM) ba sed pproach by assuming that the different (frequency) streams representing the speech signal are pro c ssed by different (independent) “experts”, each expert focusing on a different characteristic of the signal , and that the different stream likelihoods (or posteriors) are combined at some (temporal) stage to yield a globa l recognition output. As a further extension to multi-stream ASR, we will finally introduce a new approach, r eferred to as HMM2, where the HMM emission probabilities are estimated via state specific feature based HMMs responsible for merging the stream information and modeling their possible correlation.