Abstract
In this paper, we introduce a fast estimate algorithm for discriminant training of semi-continuous HMM (Hidden Markov Models).
We first present the Frame Discrimination (FD) method proposed in [1] for weight re-estimate. Then, the weight update equation is formulated in the specific framework of semi-continuous models. Finally, we propose an approximated update function which requires a very low level of computational resources.
The first experiments validate this method by comparing our fast discriminant weighting (FDW) to the original one. We observe that, on a digit recognition task, FDW and FD estimate obtain similar results, when our method decreases significantly the computational time.
A second experiment evaluates FDW in Large Vocabulary Continuous Speech Recognition (LVCSR) task. We incorporate semi-continuous FDW models in a Broadcast News (BN) transcription system. Experiments are carried out in the framework of ESTER evaluation campaign ([12]). Results show that in particular context of very compact acoustic models, discriminant weights improve the system performance compared to both a baseline continuous system and a SCHMM trained by MLE algorithm.
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Linarès, G., Lévy, C. (2007). Fast Discriminant Training of Semi-continuous HMM. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2007. Lecture Notes in Computer Science(), vol 4629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74628-7_52
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DOI: https://doi.org/10.1007/978-3-540-74628-7_52
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