An accurate silence-unvoiced-voiced classification and pitch detection algorithm is described and its implementation for real-time applications on a Texas Instruments TMS320C25 digital signal processor is evaluated. Speech classification is separated into silence detection and voice-unvoiced classification. Only the signal's energy level and zero-crossing rate are used in both classification processes. Pitch detection need only operate on voiced periods of speech. A peak picking technique is used to successively home in on the peaks that bound the pitch periods. Tests are performed on the found peaks to ensure that they are pitch-period peaks. A real-time implementation strategy is developed that combines silence detection with the signal acquisition and tightly couples voiced-unvoiced classification with pitch detection. The silence detection task is interrupt-driven and the pitch detection task loops continuously. The execution speed and accuracy results for this algorithm are shown to compare favorably with those for other such algorithms published in the literature.
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