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- W2113171330 abstract "In the context of continuous density hidden Markov model (CDHMM) we present a unified maximum likelihood (ML) approach to acoustic mismatch compensation. This is achieved by introducing additive Gaussian biases at the state level in both the mel cepstral and linear spectral domains. Flexible modelling of different mismatch effects can be obtained through appropriate bias tying. A maximum likelihood approach for joint estimation of both mel cepstral and linear spectral biases from the observed mismatched speech given only one set of clean speech models is presented, where the obtained bias estimates are used for the compensation of clean speech models during decoding. The proposed approach is applied to the recognition of noisy Lombard speech, and significant improvement in the word recognition rate is achieved." @default.
- W2113171330 created "2016-06-24" @default.
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- W2113171330 date "2002-11-22" @default.
- W2113171330 modified "2023-09-26" @default.
- W2113171330 title "A unified maximum likelihood approach to acoustic mismatch compensation: application to noisy Lombard speech recognition" @default.
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- W2113171330 doi "https://doi.org/10.1109/icassp.1997.596064" @default.
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