HMM modeling for speaker independent voice dialing in car environment

The authors describe the development of a speaker-independent isolated word recognizer for a voice dialing application operating in the car environment. Speaker-dependent and speaker-independent approaches are addressed and compared. Simple continuous hidden Markov models (HMMs) are used for speaker-dependent recognition; multiple codebook discrete and continuous HMMs are trained by speaker-independent reference data derived from a large database of speech collected inside several cars under a wide variety of driving conditions and by a large number of speakers from different Italian regions. By modeling separately two models (one for male and one for female speakers) for each word with 12 state continuous density whole word HMMs with eight diagonal covariance Gaussians per state, and performing a beam search Viterbi decoding a recognition rate of 99% has been obtained (65 errors out of 6423 words)