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Mixture Gaussian HMM-trajctory method using likelihood compensation | IEEE Conference Publication | IEEE Xplore

Mixture Gaussian HMM-trajctory method using likelihood compensation


Abstract:

We propose a new speech recognition method (HMM-trajectory method) that generates a speech trajectory from HMMs by maximizing their likelihood while accounting for the re...Show More

Abstract:

We propose a new speech recognition method (HMM-trajectory method) that generates a speech trajectory from HMMs by maximizing their likelihood while accounting for the relationship between the MFCCs and dynamic MFCCs. One major advantage of this method is that this relationship, ignored in conventional speech recognition, is directly used in the speech recognition phase. This paper improves the recognition performance of the HMMtrajectory method for dealing with mixture Gaussian distributions. While the HMM-trajectory method chooses the Gaussian distribution sequence of the HMM states by selecting the best Gaussian distribution in the state during Viterbi decoding and calculating HMM trajectory likelihood along with the sequence, the proposed method compensates for HMM trajectory likelihood using ordinary HMM likelihood. In speaker-independent speech recognition experiments, the proposed method reduced the error rate about 10% for the task compared with HMMs, proving its effectiveness for Gaussian mixture components.
Date of Conference: 09-13 December 2007
Date Added to IEEE Xplore: 14 January 2008
ISBN Information:
Conference Location: Kyoto, Japan

References

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