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arXiv · 2501.17332

Compact Neural TTS Voices for Accessibility

Kunal Jain·Eoin Murphy·Deepanshu Gupta·Jonathan Dyke·Saumya Shah·Vasilieios Tsiaras·Petko Petkov·Alistair Conkie
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Abstract

Contemporary text-to-speech solutions for accessibility applications can typically be classified into two categories: (i) device-based statistical parametric speech synthesis (SPSS) or unit selection (USEL) and (ii) cloud-based neural TTS. SPSS and USEL offer low latency and low disk footprint at the expense of naturalness and audio quality. Cloud-based neural TTS systems provide significantly better audio quality and naturalness but regress in terms of latency and responsiveness, rendering these impractical for real-world applications. More recently, neural TTS models were made deployable to run on handheld devices. Nevertheless, latency remains higher than SPSS and USEL, while disk footprint prohibits pre-installation for multiple voices at once. In this work, we describe a high-quality compact neural TTS system achieving latency on the order of 15 ms with low disk footprint. The proposed solution is capable of running on low-power devices.

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Kunal Jain, Eoin Murphy, Deepanshu Gupta, Jonathan Dyke, Saumya Shah, Vasilieios Tsiaras, Petko Petkov, Alistair Conkie. 2025-01-28. Compact Neural TTS Voices for Accessibility. https://arxiv.org/abs/2501.17332

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