A recognition method with parametric trajectory synthesized using direct relations between static and dynamic feature vector time series
書誌事項
- 公開日
- 2002-05-01
- DOI
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- 10.1109/icassp.2002.5743952
- 公開者
- IEEE
説明
Parametric trajectory models have been proposed to exploit this time-dependency. However, parametric trajectory modeling methods are unable to take advantage of efficient HMM training and recognition methods. We have proposed a new speech recognition technique that generates a speech trajectory using an HMM-based speech synthesis method. This method generates an acoustic trajectory by maximizing the likelihood of the trajectory while taking into account the relation between the cepstrum, delta-cepstrum, and delta-delta cepstrum. In this paper, we extend our method to a general formulation including variance training procedure. Speaker independent speech recognition experiments show that the proposed method is effective for speech recognition.
収録刊行物
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- IEEE International Conference on Acoustics Speech and Signal Processing
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IEEE International Conference on Acoustics Speech and Signal Processing I-957, 2002-05-01
IEEE