Pronunciation variants across system configuration, language and speaking style
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Interaction between the native and second language phonetic subsystems
Speech Communication
An introduction to variable and feature selection
The Journal of Machine Learning Research
Automatic Dialect Identification: A Study of British English
Speaker Classification II
Language and variety verification on broadcast news for Portuguese
Speech Communication
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This paper focuses on foreign accent characterisation and identification in French. How many accents may a native French speaker recognise and which cues does (s)he use? Our interest concentrates on French productions stemming from speakers of six different mother tongues: Arabic, English, German, Italian, Portuguese and Spanish, also compared with native French speakers (from the Ile-de-France region). Using automatic speech processing, our objective is to identify the most reliable acoustic cues distinguishing these accents, and to link these cues with human perception. We measured acoustic parameters such as duration and voicing for consonants, the first two formant values for vowels, word-final schwa-related prosodic features and the percentages of confusions obtained using automatic alignment including non-standard pronunciation variants. Machine learning techniques were used to select the most discriminant cues distinguishing different accents and to classify speakers according to their accents. The results obtained in automatic identification of the different linguistic origins under investigation compare favourably to perceptual data. Major identified accent-specific cues include the devoicing of voiced stop consonants, /b/ ~/v/ and /s / ~/z/ confusions, the ''rolled r'' and schwa fronting or raising. These cues can contribute to improve pronunciation modeling in automatic speech recognition of accented speech.