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Effect of TTS Generated Audio on OOV Detection and Word Error Rate in ASR for Low-resource Languages

Out-of-Vocabulary (OOV) detection and recovery is an important aspect of reducing Word Error Rate (WER) in Automatic Speech Recognition (ASR). In this paper, we evaluate the effect of OOV detection and recovery for a low-resource language on WER. We use a small seed corpus of continuous speech and improve the vocabulary by incorporating the detected OOV words. We use a syllable-model to learn OOV words and augment the word-model with these words leading to improved recognition. Our research investigates the effect on OOV detection and recovery after adding missing syllable sounds in the syllable model using a Text-to-Speech (TTS) system. Our experiments are conducted using 5 hours of continuous speech Kannada corpus. We use an already available Festival TTS for Hindi to generate Kannada speech. Our initial experiments report an improvement in OOV detection due to addition of missing syllable sounds using a cross-lingual TTS system.