A projection-based likelihood measure for speech recognition in noise

Investigates a projection-based likelihood measure that significantly improves automatic speech recognition performance in the presence of additive broadband noise. The measure was developed by modifying likelihood scores in continuous Gaussian density hidden Markov models (HMMs), resulting in the weighted projection measure (WPM). Experimental results using the proposed measure are reported for several performance factors: different cepstral-based parameters, normal and multistyle speech, and various noise signals, including white, jittering white, and broadband colored noise. In all cases, significant improvements in speaker-dependent, isolated word recognition were achieved using the WPM instead of the standard Gaussian likelihood measure (weighted Euclidean distance (WED)). As an example, at a SNR of 5 dB, the WPM resulted in improvement in recognition accuracy from 19.4 to 80.6% compared with the standard WED for the DFT mel-cepstral representation.