# Compact Neural TTS Voices for Accessibility

arXiv: https://arxiv.org/abs/2501.17332 · HTML: https://mlsanity.com/paper/2501.17332/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 28 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models](https://mlsanity.com/paper/2106.10199/) (2021)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [Efficient Neural Audio Synthesis](https://mlsanity.com/paper/1802.08435/) (2018)
- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation](https://mlsanity.com/paper/2006.10369/) (2020)
- [Lessons on Parameter Sharing across Layers in Transformers](https://mlsanity.com/paper/2104.06022/) (2021)
- [EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation](https://mlsanity.com/paper/2202.07959/) (2022)
- [LiveSpeech: Low-Latency Zero-shot Text-to-Speech via Autoregressive Modeling of Audio Discrete Codes](https://mlsanity.com/paper/2406.02897/) (2024)
- [On-device neural speech synthesis](https://mlsanity.com/paper/2109.08710/) (2021)
- [Scalable Multilingual Frontend for TTS](https://mlsanity.com/paper/2004.04934/) (2020)

## Built on by (most cited first, 2 of 2)

- [Audio Turing Test: Benchmarking the Human-likeness of Large Language Model-based Text-to-Speech Systems in Chinese](https://mlsanity.com/paper/2505.11200/) (2025)
- [Same Bit Width, Different Outcomes: Post-Training Quantization of Text-to-Speech Across Architectures](https://mlsanity.com/paper/2609.28974/) (2026)

## Abstract (arXiv)

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.