# What the Future Brings: Investigating the Impact of Lookahead for Incremental Neural TTS

arXiv: https://arxiv.org/abs/2009.02035 · HTML: https://mlsanity.com/paper/2009.02035/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2020.

## Built on by (most cited first, 9 of 9)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [LLaMA-Omni: Seamless Speech Interaction with Large Language Models](https://mlsanity.com/paper/2409.06666/) (2024)
- [Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input](https://mlsanity.com/paper/2102.09914/) (2021)
- [Review of end-to-end speech synthesis technology based on deep learning](https://mlsanity.com/paper/2104.09995/) (2021)
- [From Start to Finish: Latency Reduction Strategies for Incremental Speech Synthesis in Simultaneous Speech-to-Speech Translation](https://mlsanity.com/paper/2110.08214/) (2021)
- [Low-Latency Incremental Text-to-Speech Synthesis with Distilled Context Prediction Network](https://mlsanity.com/paper/2109.10724/) (2021)
- [Efficient Incremental Text-to-Speech on GPUs](https://mlsanity.com/paper/2211.13939/) (2022)
- [Streaming T5-based Text-to-Speech Synthesis with Limited Lookahead](https://mlsanity.com/paper/2606.21882/) (2026)
- [X2-NativeCursor: Native-Token Text Progress Tracking for Incremental-Text Streaming Codec TTS](https://mlsanity.com/paper/2609.09677/) (2026)

## Abstract (arXiv)

In incremental text to speech synthesis (iTTS), the synthesizer produces an audio output before it has access to the entire input sentence. In this paper, we study the behavior of a neural sequence-to-sequence TTS system when used in an incremental mode, i.e. when generating speech output for token n, the system has access to n + k tokens from the text sequence. We first analyze the impact of this incremental policy on the evolution of the encoder representations of token n for different values of k (the lookahead parameter). The results show that, on average, tokens travel 88% of the way to their full context representation with a one-word lookahead and 94% after 2 words. We then investigate which text features are the most influential on the evolution towards the final representation using a random forest analysis. The results show that the most salient factors are related to token length. We finally evaluate the effects of lookahead k at the decoder level, using a MUSHRA listening test. This test shows results that contrast with the above high figures: speech synthesis quality obtained with 2 word-lookahead is significantly lower than the one obtained with the full sentence.