Tue.P5b.02 Model-based Approaches to Adaptive Training in Reverberant Environments
نویسنده
چکیده
Adaptive training is a powerful approach for building speech recognition systems using non-homogeneous data. This work presents an extension of model-based adaptive training to handle reverberant environments. The recently proposed Reverberant VTS-Joint (RVTSJ) adaptation is used to factor out unwanted additive and reverberant noise variations in multi-conditional training data, yielding a canonical model neutral to noise conditions. A maximum likelihood estimation of the canonical model parameters is described. An initialisation scheme that uses the VTS-based adaptive training to initialise the model parameters is also presented. Experiments are conducted on a reverberant simulated AURORA4 task.
منابع مشابه
Model-based approaches to adaptive training in reverberant environments
Adaptive training is a powerful approach for building speech recognition systems using non-homogeneous data. This work presents an extension of model-based adaptive training to handle reverberant environments. The recently proposed Reverberant VTS-Joint (RVTSJ) adaptation[1] is used to factor out unwanted additive and reverberant noise variations in multiconditional training data, yielding a ca...
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