Accent classification for speech recognition
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Advances in phone-based modeling for automatic accent classification
IEEE Transactions on Audio, Speech, and Language Processing
Developing objective measures of foreign-accent conversion
IEEE Transactions on Audio, Speech, and Language Processing
International Journal of Speech Technology
Robust and optimum features for persian accent classification using artificial neural network
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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Variability of speaker accent is a challenge for effective human communication as well as speech technology including automatic speech recognition and accent identification. The motivation of this study is to contribute to a deeper understanding of accent variation across speakers from a cognitive perspective. The goal is to provide perceptual assessment of accent variation in native and English. The main focus is to investigate how listener's accent background affects accent perception and comprehensibility. The results from perceptual experiments show that the listeners' accent background impacts their ability to categorize accents. Speaker accent type affects perceptual accent classification. The interaction between listener accent background and speaker accent type is significant for both accent perception and speech comprehension. In addition, the results indicate that the comprehensibility of the speech contributes to accent perception. The outcomes point to the complex nature of accent perception, and provide a foundation for further investigation on the involvement of cognitive processing for accent perception. These findings contribute to a richer understanding of the cognitive aspects of accent variation, and its application for speech technology.