Hemant Kumar Kathania

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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(More)
Transcribing children's speech using acoustic models trained on adults’ speech is very challenging. In such conditions, a highly degraded recognition performance is reported due to large mismatch in the acoustic/linguistic attributes of the training and test data. The differences in pitch (or fundamental frequency) between the two groups of(More)
The task of transcribing children’s speech using statistical models trained on adults’ speech is very challenging. Large mismatch in the acoustic and linguistic attributes of the training and test data is reported to degrade the performance. In such speech recognition tasks, the differences in pitch (or fundamental frequency) between the two groups of(More)
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