Abstract
Changes in spectral characteristics are important cues for discriminating and identifying speech sounds. These changes can occur over very short time intervals. Computing frames every 10 ms, as commonly done in recognition systems, is not sufficient to capture such dynamic changes. In this paper, we propose a variable frame rate (VFR) algorithm. The algorithm results in an increased number of frames for rapidly-changing segments with relatively high energy and less frames for steady-state segments. The current implementation used an average data rate which is less than 100 frames per second. For an isolated word recognition task, and using an HMM-based speech recognition system, the proposed technique results in significant improvements in recognition accuracy especially at low signal-to-noise ratios. The technique was evaluated with mel frequency cepstral coefficient (MFCC) vectors and MFCC vectors with enhanced peak isolation.
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