1. Development of a diacritic-aware large vocabulary automatic speech recognition for Hausa language.
- Author
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Abubakar, Abdulqahar Mukhtar, Gupta, Deepa, and Vekkot, Susmitha
- Subjects
AFRICAN languages ,DEEP learning ,VOCABULARY ,SPEECH perception ,AUTOMATIC speech recognition ,ERROR rates ,LANGUAGE & languages - Abstract
Research on voice recognition for African languages is limited due to the scarcity of digital resources for training and adaptation, despite its broad usefulness. The Hausa language, spoken by almost fifty million inhabitants in West and Central Africa, is an example of a linguistic domain that has not been thoroughly studied. The Hausa language employs diacritics, which are symbols located above alphabetical characters to convey further information. By removing diacritics, the number of homographs increases, making it difficult to distinguish between similar words. This paper presents a study on speech recognition in the Hausa Language, specifically focusing on diacritized words. The study utilises the state-of-the-art wave2vec2.0 and Whisper deep learning architecture models, for transcribing audio signals into corresponding Hausa text. According to the results obtained in the study, the Whisper-large deep model emerged as the best, achieving a word error rate of 4.23% representing a considerable improvement of 43.9% when compared to the existing state-of-the-art model for Hausa language speech recognition. Additionally, the Whsiper-large model demonstrated a diacritic coverage of 92%, precision of 98.87%, with a diacritic error rate of 2.1%. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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