CONF weber-ar-01-24/IDIAP Speech Recognition Using Advanced HMM2 Features Weber, Katrin Bengio, Samy Bourlard, Hervé EXTERNAL https://publications.idiap.ch/attachments/reports/2001/rr01-24.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/weber-rr-01-24 Related documents Automatic Speech Recognition and Understanding Workshop 2001 Madonna di Campiglio, Italy December 2001 IDIAP-rr 01-24 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 domain, and extracting robust formant-like features for standard ASR system. HMM2 performs a nonlinear, state-dependent 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 with frequency information. Finally, adding temporal information to the HMM2 feature vector yields further improvements. 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. REPORT weber-rr-01-24/IDIAP Speech Recognition Using Advanced HMM2 Features Weber, Katrin Bengio, Samy Bourlard, Hervé EXTERNAL https://publications.idiap.ch/attachments/reports/2001/rr01-24.pdf PUBLIC Idiap-RR-24-2001 2001 IDIAP Martigny, Switzerland Published: ASRU 2001, Madonna di Campiglio, Italy, December 2001 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 domain, and extracting robust formant-like features for standard ASR system. HMM2 performs a nonlinear, state-dependent 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 with frequency information. Finally, adding temporal information to the HMM2 feature vector yields further improvements. 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.