Tree-Structured Maximum a Posteriori Adaptation for a Segment-Based Speech Recognition System

Irina Illina 1
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper, the problem of the adaptation of a speech recognition system to a new environment is addressed. Recently, a Structural Maximum a Posteriori adaptation (SMAP) for a frame-based HMM model adaptation has been developed. In this method, acoustic model pdfs are organised in a tree and the means and variances of the pdfs are adapted using the linear transformations estimated under MAP criteria. In this paper, we extend the SMAP adaptation to a segment-based model: the Mixture Stochastic Trajectory Model (MSTM). SMAP approach is completed by the tree construction driven by adaptation data, a Minimum Description Length (MDL) structure definition of this tree and trajectory and state adaptations. On the Resource Management task, the speaker adaptation and noise adaptation experiments show that the proposed SMAP approach gives a significant improvement compared to unadapted system.
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Conference papers
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https://hal.inria.fr/inria-00100756
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Submitted on : Tuesday, September 26, 2006 - 2:50:17 PM
Last modification on : Thursday, January 11, 2018 - 6:19:55 AM

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Irina Illina. Tree-Structured Maximum a Posteriori Adaptation for a Segment-Based Speech Recognition System. 7th International Conference on Spoken Language Processing - ICSLP'02, 2002, Denver, Colorado, USA, 4 p. ⟨inria-00100756⟩

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