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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kosaka, T. Sagayama, S. |
| Copyright Year | 1994 |
| Description | Author affiliation: ATR Interpreting Telephony Res. Labs., Kyoto, Japan (Kosaka, T.) |
| Abstract | The paper proposes a tree-structured speaker clustering algorithm and discusses its application to fast speaker adaptation. By tracing the clustering tree from top to bottom, adaptation is performed step-by-step from global to local individuality of speech. This adaptation method employs successive branch selection in the speaker clustering tree rather than parameter training and hence achieves fast adaptation using only a small amount of training data. This speaker adaptation method was applied to a hidden Markov network (HMnet) and evaluated in Japanese phoneme and phrase recognition experiments, in which it significantly outperformed speaker-independent recognition methods. In the phrase recognition experiments, the method reduced the error rate by 26.6% using three phrase utterances (approximately 2.7 seconds).< |
| File Size | 328872 |
| File Format | |
| ISBN | 0780317750 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.1994.389309 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1994-04-19 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hidden Markov models Tree data structures Speech Training data Clustering algorithms Stochastic processes Humans Error analysis Smoothing methods Clustering methods |
| Content Type | Text |
| Resource Type | Article |
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