Sylvie Voisin

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This article presents the data collected and ASR systems developped for 4 sub-saharan african languages (Swahili, Hausa, Amharic and Wolof). To illustrate our methodology, the focus is made on Wolof (a very under-resourced language) for which we designed the first ASR system ever built in this language. All data and scripts are available online on our(More)
Automatic Speech Recognition (ASR) for (under-resourced) Sub-Saharan African languages faces several challenges: small amount of transcribed speech, written language normalization issues, few text resources available for language modeling, as well as specific features (tones, morphology, etc.) that need to be taken into account seriously to optimize ASR(More)
Growing digital archives and improving algorithms for automatic analysis of text and speech create new research opportunities for fundamental research in phonetics. Such empirical approaches allow statistical evaluation of a much larger set of hypothesis about phonetic variation and its conditioning factors (among them geographical / dialectal variants).(More)
This paper deals with ASR for two languages: Hausa and Wolof. Their common characteristic is to appear with vowel length contrast. In other words, two versions (short / long) of a same vowel exist in the phoneme inventory of the language. We expect that taking into account this contrast in ASR models might help and this is what we investigate in this pilot(More)
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