An Experimental Study on the Significance of Variable Frame-Length and Overlap in the Context of Children’s Speech Recognition

It is well known that the recognition performance of an automatic speech recognition (ASR) system is affected by intra-speaker as well inter-speaker variability. The differences in the geometry of vocal organs, pitch and speaking-rate among the speakers are some such inter-speaker variabilities affecting the recognition performance. A mismatch between the training and test data with respect to any of those aforementioned factors leads to increased error rates. An example of acoustically mismatched ASR is the task of transcribing children’s speech on adult data-trained system. A large number of studies have been reported earlier that present a myriad of techniques for addressing acoustic mismatch arising from differences in pitch and dimensions of vocal organs. At the same time, only a few works on speaking-rate adaptation employing timescale modification have been reported. Furthermore, those studies were performed on ASR systems developed using Gaussian mixture models. Motivated by these facts, speaking-rate adaptation is explored in this work in the context of children’s ASR system employing deep neural network-based acoustic modeling. Speaking-rate adaptation is performed by changing the frame-length and overlap during front-end feature extraction process. Significant reductions in errors are noted by speaking-rate adaptation. In addition to that, we have also studied the effect of combining speaking-rate adaptation with vocal-tract length normalization and explicit pitch modification. In both the cases, additive improvements are obtained. To summarize, relative improvements in 15–20% over the baselines are obtained by varying the frame-length and frame-overlap.