Speed perturbation and vowel duration modeling for ASR in Hausa and Wolof languages

International audienceAutomatic 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 performance. This paper tries to address some of the above challenges through the development of ASR systems for two Sub-Saharan African languages: Hausa and Wolof. First, we investigate data augmentation technique (through speed perturbation) to overcome the lack of resources. Secondly, the main contribution is our attempt to model vowel length contrast existing in both languages. For reproducible experiments, the ASR systems developed for Hausa and Wolof are made available to the research community on github. To our knowledge, the Wolof ASR system presented in this paper is the first large vocabulary continuous speech recognition system ever developed for this language

Error Analysis of Persian Learners of Hausa Language: Cognitive Approach to Errors

Error analysis is the tool to discover the cognitive processing every learner goes through in order to learn the second language. In our study, we examine the situation for Persian learners of Hausa. The results showed both transfer and inter-lingual errors.  After analyzing the inter-lingual errors, we encountered with a distinct type of error, tense compounding error. Therefore, we could build up further the cognitive processing to see where and how this error occurs. From the teaching perspective, we analyzed the areas where errors occur more frequently and we introduced some teaching strategies to facilitate students learning. Furthermore, we contrasted the phonological system between Persian and Hausa to interpret the phonological errors and to find ways to deal with these errors. Keywords: cognitive processing, teaching, inter-lingual errors, error analysis, phonology, Persian, Hausa, Afro-Asiatic languag

Speed perturbation and vowel duration modeling for ASR in Hausa and Wolof languages

International audienceAutomatic 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 performance. This paper tries to address some of the above challenges through the development of ASR systems for two Sub-Saharan African languages: Hausa and Wolof. First, we investigate data augmentation technique (through speed perturbation) to overcome the lack of resources. Secondly, the main contribution is our attempt to model vowel length contrast existing in both languages. For reproducible experiments, the ASR systems developed for Hausa and Wolof are made available to the research community on github. To our knowledge, the Wolof ASR system presented in this paper is the first large vocabulary continuous speech recognition system ever developed for this language