Automatic phoneme recognition of South African English
Herman A. Engelbrecht · SUNScholar (Stellenbosch University) · 2004
ENGLISH ABSTRACT: Automatic speech recognition applications have been developed for many languages in other countries but not much research has been conducted on developing Human Language Technology (HLT) for S.A. languages. Research has been performed on informally gathered speech data but until now a speech corpus that could be used to develop HLT for S.A. languages did not exist. With the development of the African Speech Technology Speech Corpora, it has now become possible to develop commercial applications of HLT. The two main objectives of this work are the accurate modelling of phonemes, suitable for the purposes of LVCSR, and the evaluation of the untried S.A. English speech corpus. Three different aspects of phoneme modelling was investigated by performing isolated phoneme recognition on the NTIMIT speech corpus. The three aspects were signal processing, statistical modelling of HMM state distributions and context-dependent phoneme modelling. Research has shown that the use of phonetic context when modelling phonemes forms an integral part of most modern LVCSR systems. To facilitate the context-dependent phoneme modelling, a method of constructing robust and accurate models using decision tree-based state clustering techniques is described. The strength of this method is the ability to construct accurate models of contexts that did not occur in the training data. The method incorporates linguistic knowledge about the phonetic context, in conjunction with the training data, to decide which phoneme contexts are similar and should share model parameters. As LVCSR typically consists of continuous recognition of spoken words, the contextdependent and context-independent phoneme models that were created for the isolated recognition experiments are evaluated by performing continuous phoneme recognition. The phoneme recognition experiments are performed, without the aid of a grammar or language model, on the S.A. English corpus. As the S.A. English corpus is newly created, no previous research exist to which the continuous recognition results can be compared to. Therefore, it was necessary to create comparable baseline results, by performing continuous phoneme recognition on the NTIMIT corpus. It was found that acceptable recognition accuracy was obtained on both the NTIMIT and S.A. English corpora. Furthermore, the results on S.A. English was 2 - 6% better than the results on NTIMIT, indicating that the S.A. English corpus is of a high enough quality that it can be used for the development of HLT.