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The degradation in children's speech recognition performance under mismatched condition i.e., on the adults' speech trained models is a well known problem. Apart from several other factors, this degradation is also contributed by the large difference in the pitch values of the adults' and the children's speech. MFCC is the most commonly used feature in(More)
Most commonly used model adaptation techniques employ linear/affine transformation on models/features to address the gross acoustic mismatch between the adults’ and the children’s speech data. Since all sources of acoustic mismatch may not be appropriately modeled by just linear transformation, in this work, the efficacy of our recently proposed explicit(More)
This work explores the effect of mismatches between adults' and children's speech due to differences in various acoustic correlates on the automatic speech recognition performance under mismatched conditions. The different correlates studied in this work include the pitch, the speaking rate, the glottal parameters (open quotient, return quotient, and speech(More)
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