Automatic accent classification of foreign accented australian English speech

Abstract

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.

A comparison of two unsupervised approaches to accent identification

“…[9]) and accent (e.g. [7]). These studies have generally estimated language-or accent-specific diphone probabilities using the phonetic output from the recogniser.…”

“…do not require a transcription of the speech uttered and operate within the framework of a single SI recogniser rather than using a separate recogniser for each accent (as in e.g. [7]).…”