This paper describes a speaker independent continuous Hidden Markov Model recognizer implemented on a real-time multi-DSP system. Training and recognition are based on continuous mixture density HMMs for phonemes. Context dependent triphone models are used and the Viterbi algorithm is applied for both training and recognition. The system is implemented on a workstation with an integrated multi-DSP based acoustic front-end employing three Texas Instruments TMS320C25 signal processors and a Siemens ASIC for vector quantization. In spite of the simplifications made in order to reduce the high computational requirements for the continuous mixture densities, the system has a recognition rate of 99. 5% for the speaker independent German digit task with telephone quality. Keywords: Speech Recognition, Hidden Markov Model, mixture density, speaker independent, real-time.
Bibliographic reference. Aktas, Abdulmesih / Zünkler, Klaus (1991): "Speaker independent continuous HMM-based recognition of isolated words on a real-time multi-DSP system", In EUROSPEECH-1991, 1345-1348.