A syllable-based, pseudo-articulatory approach to speech recognition
The prevailing approach to speech recognition is Hidden Markov Modelling, which yields good performance. However, it ignores phonetics, which has the potential for going beyond the acoustic variance to provide a more abstract underlying representation. The novel approach pursued in this thesis is motivated by phonetic and phonological considerations. It is based on the notion of pseudo-articulatory representations, which are abstract and idealized accounts of articulatory activity. The original work presented here demonstrates the recovery of syllable structure information from pseudo-articulatory representations directly without resorting to statistical models of phone sequences. The work is also original in its use of syllable structures to recover phonemes. This thesis presents the three-stage syllable based, pseudo-articulatory approach in detail. Though it still has problems, this research leads to a more plausible style of automatic speech recognition and will contribute to modelling and understanding speech behaviour. Additionally, it also permits a 'multithreaded' approach combining information from different processes.