Limited resource speech recognition for Nigerian English

In this study, we introduce the UISpeech corpus which consists of Nigerian-Accented English audio-visual data. The corpus captures the linguistic diversity of Nigeria with data collected from native-speakers of Yoruba, Hausa, Igbo, Tiv, Funali and others. The UIS-peech corpus comprises isolated word recordings and read speech utterances. The new corpus is intended to provide a unique opportunity to apply and expand speech processing techniques to a limited resource language. Acoustic-phonetic differences between American English (AE) and Nigerian English (NE) are studied in terms of pronunciation variations, vowel locations in the formant space, and distances between AE-trained acoustic models and models adapted to NE. A strong impact of the AE-NE acoustic mismatch on automatic speech recognition (ASR) is observed. A combination of model adaptation and extension of AE lexicon for newly established NE pronunciation variants is shown to substantially improve performance of the AE-trained ASR system in the new NE task. This study represents the first step towards incorporating speech technology in Nigerian English.