Speech recognition via phonetically-featured syllables

Speech can be naturally described by phonetic features, such as a set of acoustic phonetic features or a set of articulatory features. This thesis establi shes the effectiveness of using phonetic features in phoneme recognition by comparing a recogniser based on them to a recogniser using an established parametrisation as a baseline. The usefulness of phonetic features serves as the foundation for the subsequent modelling of syllables. Syllables are subject to fewer of the context-sensitivity effects that hamper phone-based speech recognition. I investigate the different questions involved in creating syllable models. After training a feature-based syllable recogniser, I compare the feature based syllables against a baseline. To conclude, the feature based syllable models are compared against the baseline phoneme models in word recognition. With the resultant feature-syllable models performing well in word recognition, the featuresyllables show their future potential for large vocabulary automatic speech recognition.