Hmm Modeling For Speaker Independent Voice Dialing In Car Environment

This paper describes the development of a Speaker-Independent Isolated Words 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 are used for speaker dependent recognition, while multiple codebook Discrete and Continuous Hidden Markov Models 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 8 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).