An Empirical Study on Learning Latent Representations for Emotional Speech Synthesis

2026-06-12 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Speech Technology · Depth: Advanced, quick

Summary

An empirical study addresses emotional speech synthesis (ESS) for the VLSP 2022 task, aiming to generate humanlike, natural-sounding voices with desired emotional expressions from input text. The research integrates speaker embedding and a prosody bottleneck into the FastSpeech 2 architecture. This approach enables the system to perform two key sub-tasks: first, generating emotional speech for a single speaker, and second, transferring speaking styles from a source speaker to a target speaker using neutral, non-expressive data, while crucially retaining the target speaker's identity. The work contributes to improving expressiveness control in deep learning-based text-to-speech (TTS) systems, a significant challenge in the field.

Key takeaway

For Machine Learning Engineers developing expressive text-to-speech (TTS) systems, integrating speaker embedding and a prosody bottleneck into FastSpeech 2 offers a robust method. You can apply this architecture to generate emotional speech for individual speakers or to transfer speaking styles between speakers while preserving the target speaker's unique voice identity. Prioritize this approach for enhancing the emotional nuance and naturalness of your synthesized voices, especially when tackling specific expressive speech tasks.

Key insights

Integrating speaker embedding and prosody bottleneck into FastSpeech 2 enables effective emotional speech synthesis and style transfer.

Method

The method integrates speaker embedding and a prosody bottleneck into FastSpeech 2 to generate single-speaker emotional speech and transfer speaking styles from a source to a target speaker, preserving identity.

In practice

• Generate single-speaker emotional speech.
• Transfer speaking styles between speakers.
• Retain target speaker identity during transfer.

Topics

• Emotional Speech Synthesis
• FastSpeech 2
• Speaker Embedding
• Prosody Bottleneck
• Speech Style Transfer
• Voice Identity

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• Scenema Audio: Zero-shot expressive voice cloning and speech generation [N] — Scenema Audio offers zero-shot expressive voice cloning by decoupling emotional performance from voice identity using a diffusion model.
• F3-Tokenizer: Taming Audio Autoencoder Latents for Understanding and Generation — F3-Tokenizer unifies audio understanding and generation by structuring autoencoder latents with a separate representation encoder.
• Covertly improving intelligibility with data-driven adaptations of speech timing — Targeted speech rate adjustments, not global slowing, covertly improve intelligibility across diverse listener groups.
• From Tokens to Faces: Investigating Discrete Speech Representations for 3D Facial Animation — Encoding phonetic classes in discrete speech representations improves 3D facial animation accuracy.
• Multilingual Emotional Voice Synthesis for Brazilian Portuguese: A Comparative Analysis of Fine-Tuning Approaches — Simpler fine-tuning methods can achieve high-quality multilingual emotional voice synthesis for Brazilian Portuguese.
• Gemini 3.1 Flash TTS: the next generation of expressive AI speech — Gemini 3.1 Flash TTS offers granular control over AI speech through natural language audio tags and high-quality, cost-effective generation.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.