Fast speech adaptation in linear spectral domain for additive and convolutional noise

Donghyun Kim, Dongsuk Yook

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

Abstract

In this paper, we propose a transform-based adaptation technique for robust speech recognition in unknown environments. It uses maximum likelihood spectral transform (MLST) algorithm with additive and convolutional noise parameters. Previously many adaptation algorithms have been proposed in the cepstral domain. Though the cepstral domain may be appropriate for the speech recognition, it is difficult to handle environmental noise directly in the cepstral domain. Therefore our approach deals with such noise in the linear spectral domain in which speech is directly affected by the noise. As a result, we can use a small number of noise parameters for fast adaptation. The experiments evaluated on the FFMTIMIT corpus shows promising result with only a small number of adaptation data.

Original languageEnglish
Title of host publication8th International Conference on Spoken Language Processing, ICSLP 2004
PublisherInternational Speech Communication Association
Pages2557-2560
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

Kim, D., & Yook, D. (2004). Fast speech adaptation in linear spectral domain for additive and convolutional noise. In 8th International Conference on Spoken Language Processing, ICSLP 2004 (pp. 2557-2560). International Speech Communication Association.