# In Other News: A Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data

arXiv: https://arxiv.org/abs/1904.02790 · HTML: https://mlsanity.com/paper/1904.02790/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data](https://mlsanity.com/paper/2402.08093/) (2024)
- [SynthASR: Unlocking Synthetic Data for Speech Recognition](https://mlsanity.com/paper/2106.07803/) (2021)
- [Machine Translation Verbosity Control for Automatic Dubbing](https://mlsanity.com/paper/2110.03847/) (2021)
- [Prosodic Alignment for off-screen automatic dubbing](https://mlsanity.com/paper/2204.02530/) (2022)
- [Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosody](https://mlsanity.com/paper/2206.14643/) (2022)
- [Discrete Acoustic Space for an Efficient Sampling in Neural Text-To-Speech](https://mlsanity.com/paper/2110.12539/) (2021)
- [Enhancing audio quality for expressive Neural Text-to-Speech](https://mlsanity.com/paper/2108.06270/) (2021)

## Abstract (arXiv)

Neural text-to-speech synthesis (NTTS) models have shown significant progress in generating high-quality speech, however they require a large quantity of training data. This makes creating models for multiple styles expensive and time-consuming. In this paper different styles of speech are analysed based on prosodic variations, from this a model is proposed to synthesise speech in the style of a newscaster, with just a few hours of supplementary data. We pose the problem of synthesising in a target style using limited data as that of creating a bi-style model that can synthesise both neutral-style and newscaster-style speech via a one-hot vector which factorises the two styles. We also propose conditioning the model on contextual word embeddings, and extensively evaluate it against neutral NTTS, and neutral concatenative-based synthesis. This model closes the gap in perceived style-appropriateness between natural recordings for newscaster-style of speech, and neutral speech synthesis by approximately two-thirds.