Abstract
In this paper we address the problem of building a good speech recognizer if there is only a small amount of training data available. The acoustic models can be improved by interpolation with the well-trained models of a second recognizer from a different application scenario. In our case, we interpolate a children’s speech recognizer with a recognizer for adults’ speech. Each hidden Markov model has its own set of interpolation partners; experiments were conducted with up to 50 partners. The interpolation weights are estimated automatically on a validation set using the EM algorithm. The word accuracy of the children’s speech recognizer could be improved from 74.6 % to 81.5 %. This is a relative improvement of almost 10 %.
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Steidl, S., Stemmer, G., Hacker, C., Nöth, E., Niemann, H. (2003). Improving Children’s Speech Recognition by HMM Interpolation with an Adults’ Speech Recognizer. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_76
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DOI: https://doi.org/10.1007/978-3-540-45243-0_76
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