IDIAP-RR 01-24 Speech Recognition Using Advanced HMM 2 Features
HMM2 is a particular hidden Markov model where state emission probabilities of the temporal (primary) HMM are modeled through (secondary) state-dependent frequency-based HMMs [12]. As shown in [13], a secondary HMM can also be used to extract robust ASR features. Here, we further investigate this novel approach towards using a full HMM2 as feature extractor, working in the spectral domai n, and extracting robust formant-like features for standard ASR system. HMM2 performs a nonlinear, state-depe ndent frequency warping, and it is shown that the resulting frequency segmentation actually contains particularly discriminant features. To further improve the HMM2 system, we complement the initial spectral energy vectors w ith frequency information. Finally, adding temporal information to the HMM2 feature vector yields further impro vements. These conclusions are experimentally validated on the Numbers95 database, where word error rates of 15 %, using only a 4-dimensional feature vector (3 formant-like parameters and one time index) were obtained. Acknowledgements: This work was partly supported by grant FN 2000-059169.99/1 from the Swiss National Science Foundation.