Acoustic-Phonetics Based Speech Recognition

The objective of this project is to develop a robust and high-performance speech recognitiotl system using a segment-based approach to phonetic recognition. The recognition system will eventually be integrated with natural language processing to achieve spoken lallguagc understanding. Developed a phonetic recognition front-end and achieved 77% and 71% classiilcatiou accuracy under speaker-dependent and-independent conditions, respectively, using a set of 38 context-independent models. Collaborated with researchers at SRI in the development of the MISTRI system, making explicit use of acoustic-phonetic and phonological knowledge. Developed the SUMMIT speech recognition system that incorporates auditory modelling and explicit segmentation, and achieved a speaker-independent accuracy of 87% on the DARPA 1000-word Resource Management task using 75 phoneme models. Developed probabilistic natural language system, TINA, and achieved a test-set coverage of 78% with perplexity of 42 for the Resource Management task. • Transcribed all 6300 sentences for the TIMIT database. Developed a set of research tools for the DARPA speech research community in ot'dcr to facilitate data collection, parameter computation, statistical analysis, and speech synthesis. Improve the speech recognition performance by incorporating context-dependency ia phoneme modelling. Integrate TINA into SUMMIT in order to develop spoken language understanding capabilities. Develop a back-end on the task of a Knowledgeable Navigator, and integrate it with the spoken language system. Begin hardware development, such that the system will soon be able to execute in near real-time.