The Relevance of Text and Speech Features in Automatic Non-native English Accent Identification
This provides insights for developing accent recognition systems and studying accents in language learning, but it is incremental as it applies existing methods to a specific domain.
The paper tackled automatic identification of native accents from non-native English speech using low-level audio features and n-grams from transcriptions, achieving close to 90% classification accuracy. It found that speech features are robust to prompt variation, unlike n-grams.
This paper describes our experiments with automatically identifying native accents from speech samples of non-native English speakers using low level audio features, and n-gram features from manual transcriptions. Using a publicly available non-native speech corpus and simple audio feature representations that do not perform word/phoneme recognition, we show that it is possible to achieve close to 90% classification accuracy for this task. While character n-grams perform similar to speech features, we show that speech features are not affected by prompt variation, whereas ngrams are. Since the approach followed can be easily adapted to any language provided we have enough training data, we believe these results will provide useful insights for the development of accent recognition systems and for the study of accents in the context of language learning.