Robust speech recognition via modeling spectral coefficients with HMM's with complex Gaussian components

Robust speech recognition via hidden Markov modeling of spectral vectors is studied in this paper. The hidden Markov model (HMM) mixture components are assumed complex Gaussian with zero mean, diagonal covariance, and with incorporating an unknown scalar gain term. The gain term is associated with each spectral vector and it models the varying energy of speech signals. It is estimated by applying the maximum likelihood (ML) criterion. On an isolated digit database, in clean conditions, the spectral modeling with ML gain estimation approach achieved similar performance to cepstral modeling of speech. Two additive noise compensation approaches for the spectral modeling scheme are also considered. The rst approach requires a full noise HMM. This HMM is combined with the clean speech HMM to yield a noisy speech HMM. The second approach requires only the spectral shape of the noise. A term dependent on the spectral shape, together with an unknown magnitude term, is incorporated into the clean speech HMM to yield a noisy speech HMM. The unknown magnitude of the noise is estimated via the ML criterion. The performance of these two approaches for isolated digit recognition in noise is demonstrated and compared to a robust cepstral modeling approach from the literature.