A novel speaker adaptation approach for continuous densities HMM's

Eleftherios Frangoulis, Victoria Sgardoni

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)


An approach for speaker adaptation aiming to get high recognition performance from an HMM speech recognizer after a short training session with a new speaker is presented. The technique presented exploits the Gaussian multivariate nature of continuous density HMM distributions, to adapt the model parameters. This adaptation technique was applied to a 20-word vocabulary. It was tested on 70 new speakers after a training session of 2 to 5 repetitions of each vocabulary word. The experiments carried out have shown a significant improvement in the recognition performance, even when only two training tokens from the new speaker are used.
Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing 1991, ICASSP 91
Place of PublicationToronto
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages861 - 864
Publication statusPublished - 14 May 1991


  • hidden Markov models (HMMs)
  • speech recognition
  • HMM speech recogniser
  • continuous densities HMMs


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