In this paper we describe the algorithms used in the BBN BYBLOS Continuous Speech Recognition system. The BYBLOS system uses context-dependent hidden Markov models of phonemes to provide a robust model of phonetic coarticulation. We provide an update of the ongoing research aimed at improving the recognition accuracy. In the first experiment we confirm the large improvement in accuracy that can be derived by using spectral derivative parameters in the recognition. In particular, the word error rate is reduced by a factor of two. Currently the system achieves a word error rate of 2.9% when tested on the speaker-dependent part of the standard 1000-Word DARPA Resource Management Database using the Word-Pair grammar supplied with the database. When no grammar was used, the error rate is 15.3%. Finally, we present a method for smoothing the discrete densities on the states of the HMM, which is intended to alleviate the problem of insufficient training for detailed phonetic models.
We address the problem of increasing the intelligibility of a 300 b/s segment vocoder by investigating: 1) new LSP-based distance measures and 2) new structures and construction methods for segment codebooks. We evaluate a variety of new distance measures and find that, after tuning, all of the distance measures provide almost equal intelligibility, indicating that some other factor, such as codebook template quality, is limiting performance. In an effort to improve the codebook, we examine multiple duration-dependent codebooks constructed by selecting phonetically-labelled segments from the TIMIT database.
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