Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/6BAF4883-036A-47B4-8A7B-A640F68EB6C9 | ||
| Year | 1996 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | V. Digalakis, P. Monaco and H. Murveit, "Genones: generalized mixture tying in continuous hidden Markov model-based speech recognizers," IEEE Trans. Speech Audio Process., vol. 4, no. 4, pp. 281-289, Jul. 1996. doi:10.1109/89.506931 https://doi.org/10.1109/89.506931 | ||
| Appears in Collections |
An algorithm is proposed that achieves a good tradeoff 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-significantly reduce the number of Gaussian densities that are evaluated with little or no impact on speech recognition accuracy
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