Shweta Ghai

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This work is motivated by our earlier study which shows that on explicit pitch normalization the children’s speech recognition performance on the adults’ speech trained models improves as a result of reduction in the pitch-dependent distortions in the spectral envelope. In this paper, we study the role of spectral smoothing in context of children’s speech(More)
In this work, we have studied the effect of pitch variations across the speech signals in context of automatic speech recognition. Our initial study done on vowel data indicates that on account of insufficient smoothing of pitch harmonics by the filterbank, particularly for high pitch signals, the variances of mel frequency cepstral coefficients (MFCC)(More)
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)
In this work, following our previous studies, we study and quantify the effect of pitch on LPCC and PLPC features and explore their efficacy for children’s mismatched ASR in comparison to MFCC. Our analysis shows that, unlike MFCC, LPCC feature has no major influence of pitch variations. On the other hand, similar to MFCC, though PLPC is also found to be(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)
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)
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