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%. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Steidl, S., Stemmer, G., Hacker, C., Nöth, E., & Niemann, H. (2003). Improving children’s speech recognition by HMM interpolation with an adults’ speech recognizer. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2781, 600–607. https://doi.org/10.1007/978-3-540-45243-0_76
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