Genones: generalized mixture tying in continuous hidden Markov model-based speech recognizers

An algorithm is proposed that achieves a good trade-oo between modeling resolution and robustness by using a new, general scheme for tying of mixture components in continuous mixture-density hidden Markov model (HMM)-based speech recognizers. The sets of HMM states that share the same mixture components are determined automatically using agglomerative clustering techniques. Experimental results on ARPA's Wall-Street Journal corpus show that this scheme reduces errors by 25% over typical tied-mixture systems. New fast algorithms for computing Gaussian likelihoods{the most time-consuming aspect of continuous-density HMM systems{are also presented. These new algorithms signiicantly reduce the number of Gaussian densities that are evaluated with little or no impact on speech recognition accuracy.