Performance of isolated word recognition system for confusable vocabulary

In this paper we discuss some of the limitations of the existing isolated word speech recognition system (IWSR) when applied to confusable vocabulary. For our study we have chosen a subset of Hindi stop consonants as the confusable word set. The members of this set differ among themselves primarily in the short leading consonant part and at the interface of the consonant and the following dominant vowel part. We adopt a signal-dependent approach for parameter extraction and matching strategy. This approach gives better performance compared with the conventional approach, but the performance still falls far short of the desired goal of 100% recognition. Refined signal processing suitable for appropriate segments of speech appear to be the way out of this problem. We discuss our studies in this direction. We use a new measure, called Performance Index, to evaluate the changes in performance due to innovations carried out on small data sets.