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Speaker-normalization and -adaptation methods are essential components of state-of-the-art speech recognition systems nowadays. Recently, so-called invariant integration features were presented which are motivated by the theory of invariants. While it was shown that the integration features outperform MFCCs when used with a basic monophone recognition system, it was left open, if their benefits still can be observed when a more sophisticated recognition system with speaker-normalization and/or speaker-adaptation components is used. This work investigates the combination of the integration features with standard speaker-normalization and -adaptation methods. We show that the integration features benefit from adaptation methods and significantly outperform MFCCs in matching, as well as in mismatching training-test conditions.
|Number of pages
|Published - 01.09.2010
|11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All - Makuhari, Japan
Duration: 26.09.2010 → 30.09.2010
Conference number: 85334
|11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All
|26.09.10 → 30.09.10
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- 1 Finished
01.01.07 → 31.12.11
Project: DFG Projects › DFG Individual Projects