Compact acoustic model for embedded implementation

Junho Park, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

An acoustic model for an embedded speech recognition system must exhibit two desirable features; ability to minimize performance degradation in recognition while solving the memory problem under limited system resources. To cope with the challenges, we introduce the state-clustered tied-mixture (SCTM) HMM as an acoustic model optimization. The proposed SCTM modeling shows a significant improvement in recognition performance as well as a solution to sparse training data problem. Moreover, the state weight quantizing method achieves a drastic reduction in model size. In this paper, we describe the acoustic model optimization procedure for embedded speech recognition system and corresponding performance evaluation results.

Original languageEnglish
Title of host publication8th International Conference on Spoken Language Processing, ICSLP 2004
PublisherInternational Speech Communication Association
Pages693-696
Number of pages4
Publication statusPublished - 2004
Event8th International Conference on Spoken Language Processing, ICSLP 2004 - Jeju, Jeju Island, Korea, Republic of
Duration: 2004 Oct 42004 Oct 8

Other

Other8th International Conference on Spoken Language Processing, ICSLP 2004
CountryKorea, Republic of
CityJeju, Jeju Island
Period04/10/404/10/8

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ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Park, J., & Ko, H. (2004). Compact acoustic model for embedded implementation. In 8th International Conference on Spoken Language Processing, ICSLP 2004 (pp. 693-696). International Speech Communication Association.