# Controllable speech synthesis by learning discrete phoneme-level prosodic representations

arXiv: https://arxiv.org/abs/2211.16307 · HTML: https://mlsanity.com/paper/2211.16307/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 60 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 30 of 45)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron](https://mlsanity.com/paper/1803.09047/) (2018)
- [FastPitch: Parallel Text-to-speech with Pitch Prediction](https://mlsanity.com/paper/2006.06873/) (2020)
- [Hierarchical Generative Modeling for Controllable Speech Synthesis](https://mlsanity.com/paper/1810.07217/) (2018)
- [Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning](https://mlsanity.com/paper/1907.04448/) (2019)
- [Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling](https://mlsanity.com/paper/2010.04301/) (2020)
- [Learning latent representations for style control and transfer in end-to-end speech synthesis](https://mlsanity.com/paper/1812.04342/) (2018)
- [Robust and fine-grained prosody control of end-to-end speech synthesis](https://mlsanity.com/paper/1811.02122/) (2018)
- [Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis](https://mlsanity.com/paper/1910.10288/) (2019)
- [Mellotron: Multispeaker expressive voice synthesis by conditioning on rhythm, pitch and global style tokens](https://mlsanity.com/paper/1910.11997/) (2019)
- [MelNet: A Generative Model for Audio in the Frequency Domain](https://mlsanity.com/paper/1906.01083/) (2019)
- [Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis](https://mlsanity.com/paper/2002.03785/) (2020)
- [Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior](https://mlsanity.com/paper/2002.03788/) (2020)
- [Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis](https://mlsanity.com/paper/1808.01410/) (2018)
- [Fine-grained robust prosody transfer for single-speaker neural text-to-speech](https://mlsanity.com/paper/1907.02479/) (2019)
- [CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network](https://mlsanity.com/paper/1905.07195/) (2019)
- [CopyCat: Many-to-Many Fine-Grained Prosody Transfer for Neural Text-to-Speech](https://mlsanity.com/paper/2004.14617/) (2020)
- [Semi-Supervised Generative Modeling for Controllable Speech Synthesis](https://mlsanity.com/paper/1910.01709/) (2019)
- [Controllable neural text-to-speech synthesis using intuitive prosodic features](https://mlsanity.com/paper/2009.06775/) (2020)
- [High Quality Streaming Speech Synthesis with Low, Sentence-Length-Independent Latency](https://mlsanity.com/paper/2111.09052/) (2021)
- [Bunched LPCNet : Vocoder for Low-cost Neural Text-To-Speech Systems](https://mlsanity.com/paper/2008.04574/) (2020)
- [Hierarchical Prosody Modeling for Non-Autoregressive Speech Synthesis](https://mlsanity.com/paper/2011.06465/) (2020)
- [Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis](https://mlsanity.com/paper/1906.03402/) (2019)
- [Fine-grained style control in Transformer-based Text-to-speech Synthesis](https://mlsanity.com/paper/2110.06306/) (2021)
- [A Neural Parametric Singing Synthesizer](https://mlsanity.com/paper/1704.03809/) (2017)
- [Adversarially learning disentangled speech representations for robust multi-factor voice conversion](https://mlsanity.com/paper/2102.00184/) (2021)
- [CAMP: a Two-Stage Approach to Modelling Prosody in Context](https://mlsanity.com/paper/2011.01175/) (2020)

## Built on by (most cited first, 3 of 3)

- [Voice Cloning: Comprehensive Survey](https://mlsanity.com/paper/2505.00579/) (2025)
- [PRESENT: Zero-Shot Text-to-Prosody Control](https://mlsanity.com/paper/2408.06827/) (2024)
- [Pseudo-Cepstrum: Pitch Modification for Mel-Based Neural Vocoders](https://mlsanity.com/paper/2512.16519/) (2025)

## Abstract (arXiv)

In this paper, we present a novel method for phoneme-level prosody control of F0 and duration using intuitive discrete labels. We propose an unsupervised prosodic clustering process which is used to discretize phoneme-level F0 and duration features from a multispeaker speech dataset. These features are fed as an input sequence of prosodic labels to a prosody encoder module which augments an autoregressive attention-based text-to-speech model. We utilize various methods in order to improve prosodic control range and coverage, such as augmentation, F0 normalization, balanced clustering for duration and speaker-independent clustering. The final model enables fine-grained phoneme-level prosody control for all speakers contained in the training set, while maintaining the speaker identity. Instead of relying on reference utterances for inference, we introduce a prior prosody encoder which learns the style of each speaker and enables speech synthesis without the requirement of reference audio. We also fine-tune the multispeaker model to unseen speakers with limited amounts of data, as a realistic application scenario and show that the prosody control capabilities are maintained, verifying that the speaker-independent prosodic clustering is effective. Experimental results show that the model has high output speech quality and that the proposed method allows efficient prosody control within each speaker's range despite the variability that a multispeaker setting introduces.