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Improving the recognition performance by using a parallel-branch subunit model based on misrecognised data

Improving the recognition performance by using a parallel-branch subunit model based on misrecognised data

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An initialisation and training method using a parallel-branch subunit model is obtained which uses a continuous hidden Markov model for improved recognition performance. The model is obtained by adding a new subunit branch based on misrecognised data in the training data to the previous parallel branches for that subunit. This procedure is shown to be efficient and gives good word recognition performance.

References

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      • B.H. Juang , L.R. Rabiner . The segmental k-means algorithm for estimating parameters of hidden Markovmodels. IEEE Trans. , 1639 - 1641
    2. 2)
      • Bahl, L.R., Brown, P.F., Desouza, P.V., Mercer, R.L.: `A new algorithm for the estimation of hidden Markov model parameters', ICASSP 88, 1988, New York, p. 493–496.
    3. 3)
      • Rabiner, L.R., Lee, C.H., Juang, B.H., Wilpon, J.G.: `HMM clustering for connected word recognition', IEEE Int. Conf. on Acoustics, Speech, And Signal Processing, 1989, p. 405–408.
    4. 4)
      • C.H. Lee , L.R. Rabiner , R. Pieraccini , J.G. Wilpon . Acoustic modeling for large vocabulary speech recognition. Computer speech and Language , 127 - 165
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