English speech based on accent dependent parallel phoneme recognition (PPR) has been developed. The classifier is designed to process continuous speech and to discriminate between native Australian English (AuE) speakers and two migrant speaker groups with foreign accents, whose first languages are Lebanese Arabic (LA) and South Vietnamese (SV). The training of the system can be automated and is novel in that it does not require manually labelled accented data. The test utterances are processed in parallel by three (AuE, SV and LA) accent-specific recognizers incorporating the accent-specific HMMs and phoneme bigram language models to produce accent discrimination likelihood scores. The best average accent classification rates were 85.3% and 76.6% for accent pair and three accent class discrimination tasks, respectively. Analyses of the contributions to accent discrimination by the phoneme level processing, and by the language model, are described.

An automatic classification system for foreign accents in Australian English speech based on accent dependent parallel phoneme recognition (PPR) has been developed. The classifier is designed to process continuous speech and to discriminate between native Australian English (AuE) speakers and two migrant speaker groups with foreign accents, whose first languages are Lebanese Arabic (LA) and South Vietnamese (SV). The training of the system can be automated and is novel in that it does not require manually labelled accented data. The test utterances are processed in parallel by three (AuE, SV and LA) accent-specific recognizers incorporating the accent-specific HMMs and phoneme bigram language models to produce accent discrimination likelihood scores. The best average accent classification rates were 85.3% and 76.6% for accent pair and three accent class discrimination tasks, respectively. Analyses of the contributions to accent discrimination by the phoneme level processing, and by the language model, are described.

Abstract. Large databases are useful tools for speech technology research. Their usefulness is greatly enhanced if the data is annotated with time aligned labels. This is expensive and time consuming and has lead to the investigation and development of automatic aligners. This paper reports on an automatic aligner developed initially to solve the problem of annotating a large database within a set period of time. While developing the aligner, we investigated the importance of the models, the use of manual labels to bootstrap the system, and the role of the dictionary in the effectiveness of the aligner, and found that each had a contribution to make. The aligner produced was tested on unseen data to gauge its accuracy before being applied as a tool to annotation of a large amount of data. The aligner was developed in a way that facilitates its use in other applications.

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