How effective is unsupervised data collection for children's speech recognition?

Gregory Aist, Peggy Chan, X.D. Huang, L. Jiang, Rebecca Kennedy, DeWitt Talmadge Latimer, Jack Mostow, and Calvin Yeung
International Conference on Speech and Language Processing (ICSLP98), December, 1998.


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Abstract
Children present a unique challenge to automatic speech recognition. Today's state-of-the-art speech recognition systems still have problems handling children's speech because acoustic models are trained on data collected from adult speech. In this paper we describe an inexpensive way to mend this problem. We collected children's speech when they interact with an automated reading tutor. These data are subsequently transcribed by a speech recognition system and automatically filtered. We studied how to use these automatically collected data to improve children's speech recognition system's performance. Experiments indicate that automatically collected data can reduce the error rate significantly on children's speech.

Notes
Associated Lab(s) / Group(s): Project LISTEN
Associated Project(s): Project LISTEN\'s Reading Tutor

Text Reference
Gregory Aist, Peggy Chan, X.D. Huang, L. Jiang, Rebecca Kennedy, DeWitt Talmadge Latimer, Jack Mostow, and Calvin Yeung, "How effective is unsupervised data collection for children's speech recognition?," International Conference on Speech and Language Processing (ICSLP98), December, 1998.

BibTeX Reference
@inproceedings{Aist_1998_3688,
   author = "Gregory Aist and Peggy Chan and X.D. Huang and L. Jiang and Rebecca Kennedy and DeWitt Talmadge Latimer and Jack Mostow and Calvin Yeung",
   title = "How effective is unsupervised data collection for children's speech recognition?",
   booktitle = "International Conference on Speech and Language Processing (ICSLP98)",
   month = "December",
   year = "1998",
}