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Variation of features of interframe dependent HMM for speech recognition

Variation of features of interframe dependent HMM for speech recognition

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The effects are explored of using different dynamic features in conjunction with an HMM that permits a dependency on both preceeding and succeeding frames. In particular, features which capture dynamic information are varied in the interframe dependent HMM. A recognition accuracy of 93.9% for the Connex speaker-independent E-set was obtained using tied states.

Inspec keywords: hidden Markov models; speech recognition

Other keywords: interframe dependent HMM; tied states; Connex speaker-independent E-set; speech recognition; dynamic features

Subjects: Speech and audio signal processing; Other topics in statistics; Speech recognition; Other topics in statistics

References

    1. 1)
      • Harte, H., Vaseghi, S., Milner, B.: `Dynamic features for segmental speech recognition', ICSLP 96, p. 933–936.
    2. 2)
      • J. Ming , F.J. Smith . Modelling of the inteframe dependence in an HMM using conditionalGaussian mixtures. Comput. Speech and Lang. , 229 - 247
    3. 3)
      • Woodland, P.C., Cole, D.R.: `Optimizing hidden Markov models using discriminative output distributions', Proc. ICASSP '91, p. 545–548.
    4. 4)
      • Hanna, P., Ming, J., O'Boyle, P.O., Smith, F.J.: `Modelling inter-frame dependence with preceeding andsucceeding frames', Eurospeech 97, 3, p. 167–1170.
http://iet.metastore.ingenta.com/content/journals/10.1049/el_19980640
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