# Dynamic Prosody Generation for Speech Synthesis using Linguistics-Driven Acoustic Embedding Selection

arXiv: https://arxiv.org/abs/1912.00955 · HTML: https://mlsanity.com/paper/1912.00955/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 11 of 11)

- [ParaTTS: Learning Linguistic and Prosodic Cross-sentence Information in Paragraph-based TTS](https://mlsanity.com/paper/2209.06484/) (2022)
- [A learned conditional prior for the VAE acoustic space of a TTS system](https://mlsanity.com/paper/2106.10229/) (2021)
- [Discrete Acoustic Space for an Efficient Sampling in Neural Text-To-Speech](https://mlsanity.com/paper/2110.12539/) (2021)
- [Location, Location: Enhancing the Evaluation of Text-to-Speech Synthesis Using the Rapid Prosody Transcription Paradigm](https://mlsanity.com/paper/2107.02527/) (2021)
- [A Study of Modeling Rising Intonation in Cantonese Neural Speech Synthesis](https://mlsanity.com/paper/2208.02189/) (2022)
- [Investigation of Japanese PnG BERT language model in text-to-speech synthesis for pitch accent language](https://mlsanity.com/paper/2212.08321/) (2022)
- [Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken Sentences](https://mlsanity.com/paper/2303.08809/) (2023)
- [Audio-Visual Neural Syntax Acquisition](https://mlsanity.com/paper/2310.07654/) (2023)
- [Using multiple reference audios and style embedding constraints for speech synthesis](https://mlsanity.com/paper/2110.04451/) (2021)
- [Multi-Scale Spectrogram Modelling for Neural Text-to-Speech](https://mlsanity.com/paper/2106.15649/) (2021)
- [On the Cost and Benefits of Training Context with Utterance or Full Conversation Training: A Comparative Stud](https://mlsanity.com/paper/2505.07202/) (2025)

## Abstract (arXiv)

Recent advances in Text-to-Speech (TTS) have improved quality and naturalness to near-human capabilities when considering isolated sentences. But something which is still lacking in order to achieve human-like communication is the dynamic variations and adaptability of human speech. This work attempts to solve the problem of achieving a more dynamic and natural intonation in TTS systems, particularly for stylistic speech such as the newscaster speaking style. We propose a novel embedding selection approach which exploits linguistic information, leveraging the speech variability present in the training dataset. We analyze the contribution of both semantic and syntactic features. Our results show that the approach improves the prosody and naturalness for complex utterances as well as in Long Form Reading (LFR).