This paper presents the framework for an ultra low bit rate speech vocoder. The system is based on a recognition-synthesis paradigm in which a single ergodic hidden Markov model (EHMM) is used to capture the statistical characterizations of speech in a flexible manner capable of limiting the effects of recognition errors. Because predetermined speech units are not used, this system has the advantage of not requiring a transcription for the training data set. By incorporating a mixed excitation scheme based on an improved MELP formulation into the EHMM, additional gains in quality and speaker characterization are achieved at no cost to the bit rate.
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