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In the context of automatic speech recognition (ASR), the power spectrum is generally warped to the Mel-scale during front-end speech parameterization. This is motivated by the fact that human perception of sound is nonlinear. The Mel-filterbank provides better resolution for low-frequency contents, while a greater degree of averaging happens in the high-frequency range. The work presented in this...
This work presents novel approaches for reducing acoustic mismatch in case of children's speech recognition using acoustic models trained on adults' speech data. In this regard, heteroscedastic linear discriminant analysis (HLDA) based transformation of test data is explored. It is well known that HLDA reduces the dimensionality of the feature parameters and also increases the discrimination among...
The children's speech recognition performance under mismatched condition i.e., recognizing on the adults' speech trained models is a challenging task. It is well known that MFCC features contain all the information regarding speech and mismatch factor at the same time. Therefore, in this work, the truncation of MFCC features is explored for children's speech recognition on adults' speech trained models...
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