Phonological feature based variable frame rate scheme for improved speech recognition

In this paper, we propose a new scheme for variable frame rate (VFR) feature processing based on high level segmentation (HLS) of speech into broad phone classes. Traditional fixed-rate processing is not capable of accurately reflecting the dynamics of continuous speech. On the other hand, the proposed VFR scheme adapts the temporal representation of the speech signal by tying the framing strategy with the detected phone class sequence. The phone classes are detected and segmented by using appropriately trained phonological features (PFs). In this manner, the proposed scheme is capable of tracking the evolution of speech due to the underlying phonetic content, and exploiting the non-uniform information flow-rate of speech by using a variable framing strategy. The new VFR scheme is applied to automatic speech recognition of TIMIT and NTIMIT corpora, where it is compared to a traditional fixed window-size/frame-rate scheme. Our experiments yield encouraging results with relative reductions of 24% and 8% in WER (word error rate) for TIMIT and NTIMIT tasks, respectively.