A Fast Adaptation Approach for Enhanced Automatic Recognition of Children’s Speech with Mismatched Acoustic Models

This study explores issues in automatic speech recognition (ASR) of children’s speech on acoustic models trained using adults’ speech. For acoustic modeling in ASR, the employed front-end features capture the characteristics of the vocal filter while smoothing out those of the source (excitation). Adults’ and children’s speech differ significantly due to large deviation in the acoustic correlates such as pitch, formants, speaking rate, etc. In the context of children’s speech recognition on mismatched acoustic models, the recognition rates remain highly degraded despite use of the vocal tract length normalization (VTLN) for addressing formant mismatch. For commonly used mel-filterbank-based cepstral features, earlier studies have shown that the acoustic mismatch is exacerbated by insufficient smoothing of pitch harmonics for child speakers. To address this problem, a structured low-rank projection of the test features as well as that of the mean and the covariance parameters of the acoustic models was explored in an earlier work. In this paper, a low-latency adaptation scheme is presented for children’s mismatched ASR. The presented fast adaptation approach exploits the earlier reported low-rank projection technique in order to reduce the computational cost. In the proposed approach, developmental data from the children’s domain is partitioned into separate groups on the basis of their estimated VTLN warp factors. A set of adapted acoustic models is then created by combining the low-rank projection with the model space adaptation technique for each of the warp factors. Given the children’s test utterance, first an appropriate pre-adapted model mean supervector is chosen based on its estimated warp factor. The chosen supervector is then optimally scaled. Consequently, only two parameters are required to be estimated, i.e., a warp factor and a model mean scaling factor. Even with such stringent constraints, the proposed adaptation technique results in a relative improvement of about $$44\%$$44% over the VTLN included baseline.