STAT: speech transcription analysis tool
NAACL-Demonstrations '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Demonstration Session
Developing objective measures of foreign-accent conversion
IEEE Transactions on Audio, Speech, and Language Processing
User identity verification via mouse dynamics
Information Sciences: an International Journal
Phonetic unification of multiple accents for spanish and arabic languages
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
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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Apart form the word content and identity of a speaker; speech also conveys information about several soft biometric traits such as accent and gender. Accurate classification of these features can have a direct impact on present speech systems. An accent specific dictionary or word models can be used to improve accuracy of speech recognition systems. Gender and accent information can also be used to improve the performance of speaker recognition systems. In this paper, we distinguish between standard American English and Indian Accented English using the second and third formant frequencies of specific accent markers. A GMM classification is used on the feature set for each accent group. The results show that using just the formant frequencies of these accent markers is sufficient to achieve a suitable classification for these two accent groups.