Speech recognition using dynamic features of acoustic subword spectra

A novel approach for speech signal analysis has been developed that incorporates both steady-state and dynamic spectral features into a unified model. This model has been successfully applied in automatic speech recognition contexts and does not require frame-based optimal search algorithms. The model decomposes an utterance into a chain of acoustic subwords and simultaneously generates a mathematical description of instantaneous acoustic-phonetic features and dynamic transitions. The algorithm was tested using a speaker-dependent limited vocabulary recognition task and achieved higher recognition rates than both vector quantization and hidden Markov models