# Affectron: Emotional Speech Synthesis with Affective and Contextually Aligned Nonverbal Vocalizations

arXiv: https://arxiv.org/abs/2603.14432 · HTML: https://mlsanity.com/paper/2603.14432/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Emotional speech synthesis and recognition, one of 11 directions in Speech and audio language models.
- Builds on 33 of its 66 references that are in the map.
- First posted on arXiv 15 Mar 2026.

## Placed in

- Speech and audio language models / [Emotional speech synthesis and recognition](https://mlsanity.com/speech/emotional-speech-synthesis-and-recognition/)

## Builds on (most cited first, 30 of 33)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers](https://mlsanity.com/paper/2301.02111/) (2023)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [Efficient Training of Language Models to Fill in the Middle](https://mlsanity.com/paper/2207.14255/) (2022)
- [emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation](https://mlsanity.com/paper/2312.15185/) (2023)
- [CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training](https://mlsanity.com/paper/2505.17589/) (2025)
- [Dawn of the transformer era in speech emotion recognition: closing the valence gap](https://mlsanity.com/paper/2203.07378/) (2022)
- [VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild](https://mlsanity.com/paper/2403.16973/) (2024)
- [CM3: A Causal Masked Multimodal Model of the Internet](https://mlsanity.com/paper/2201.07520/) (2022)
- [Enabling Language Models to Fill in the Blanks](https://mlsanity.com/paper/2005.05339/) (2020)
- [EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation](https://mlsanity.com/paper/2406.06185/) (2024)
- [Emotion Intensity and its Control for Emotional Voice Conversion](https://mlsanity.com/paper/2201.03967/) (2022)
- [EmoSphere-TTS: Emotional Style and Intensity Modeling via Spherical Emotion Vector for Controllable Emotional Text-to-Speech](https://mlsanity.com/paper/2406.07803/) (2024)
- [Laugh Now Cry Later: Controlling Time-Varying Emotional States of Flow-Matching-Based Zero-Shot Text-to-Speech](https://mlsanity.com/paper/2407.12229/) (2024)
- [EmoSphere++: Emotion-Controllable Zero-Shot Text-to-Speech via Emotion-Adaptive Spherical Vector](https://mlsanity.com/paper/2411.02625/) (2024)
- [NVSpeech: An Integrated and Scalable Pipeline for Human-Like Speech Modeling with Paralinguistic Vocalizations](https://mlsanity.com/paper/2508.04195/) (2025)
- [NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech](https://mlsanity.com/paper/2507.13155/) (2025)
- [A Scalable Pipeline for Enabling Non-Verbal Speech Generation and Understanding](https://mlsanity.com/paper/2508.05385/) (2025)
- [Making Flow-Matching-Based Zero-Shot Text-to-Speech Laugh as You Like](https://mlsanity.com/paper/2402.07383/) (2024)
- [JVNV: A Corpus of Japanese Emotional Speech with Verbal Content and Nonverbal Expressions](https://mlsanity.com/paper/2310.06072/) (2023)
- [Effective Pre-Training of Audio Transformers for Sound Event Detection](https://mlsanity.com/paper/2409.09546/) (2024)
- [SponTTS: modeling and transferring spontaneous style for TTS](https://mlsanity.com/paper/2311.07179/) (2023)
- [DiEmo-TTS: Disentangled Emotion Representations via Self-Supervised Distillation for Cross-Speaker Emotion Transfer in Text-to-Speech](https://mlsanity.com/paper/2505.19687/) (2025)
- [Laughter Synthesis using Pseudo Phonetic Tokens with a Large-scale In-the-wild Laughter Corpus](https://mlsanity.com/paper/2305.12442/) (2023)

## Abstract (arXiv)

Nonverbal vocalizations (NVs), such as laughter and sighs, are central to the expression of affective cues in emotional speech synthesis. However, learning diverse and contextually aligned NVs remains challenging in open settings due to limited NV data and the lack of explicit supervision. Motivated by this challenge, we propose Affectron as a framework for affective and contextually aligned NV generation. Built on a small-scale open and decoupled corpus, Affectron introduces an NV-augmented training strategy that expands the distribution of NV types and insertion locations. We further incorporate NV structural masking into a speech backbone pre-trained on purely verbal speech to enable diverse and natural NV synthesis. Experimental results demonstrate that Affectron produces more expressive and diverse NVs than baseline systems while preserving the naturalness of the verbal speech stream.