Enhancing Out-of-Vocabulary Performance of Indian TTS Systems for Practical Applications through Low-Effort Data Strategies
This addresses a practical issue for users of TTS systems in low-resource Indian languages, though it is incremental as it builds on existing methods with a data-focused strategy.
The paper tackled the problem of poor out-of-vocabulary (OOV) performance in Hindi and Tamil text-to-speech systems due to limited training data, showing that using low-cost volunteer recordings to cover unseen character bigrams improved OOV intelligibility without degrading voice quality or in-domain performance.
Publicly available TTS datasets for low-resource languages like Hindi and Tamil typically contain 10-20 hours of data, leading to poor vocabulary coverage. This limitation becomes evident in downstream applications where domain-specific vocabulary coupled with frequent code-mixing with English, results in many OOV words. To highlight this problem, we create a benchmark containing OOV words from several real-world applications. Indeed, state-of-the-art Hindi and Tamil TTS systems perform poorly on this OOV benchmark, as indicated by intelligibility tests. To improve the model's OOV performance, we propose a low-effort and economically viable strategy to obtain more training data. Specifically, we propose using volunteers as opposed to high quality voice artists to record words containing character bigrams unseen in the training data. We show that using such inexpensive data, the model's performance improves on OOV words, while not affecting voice quality and in-domain performance.