Lombard Speech Synthesis for Any Voice with Controllable Style Embeddings

2026-01-19

AI Summary

This work proposes a method for synthesizing controllable Lombard-effect speech for any speaker without requiring Lombard training samples. By learning style embeddings from large-scale, prosodically diverse data and applying principal component analysis (PCA) to identify key directions associated with Lombard characteristics, the approach enables precise manipulation of the style embedding. The method supports fine-grained control over both Lombard intensity and prosody, significantly enhancing speech intelligibility in noisy environments while preserving naturalness and speaker identity. To the best of our knowledge, this is the first unsupervised, cross-speaker framework capable of generating controllable Lombard speech without any Lombard-labeled data.

Technology Category

• Natural Language Processing: Speech
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

The Lombard effect plays a key role in natural communication, particularly in noisy environments or when addressing hearing-impaired listeners. We present a controllable text-to-speech (TTS) system capable of synthesizing Lombard speech for any speaker without requiring explicit Lombard data during training. Our approach leverages style embeddings learned from a large, prosodically diverse dataset and analyzes their correlation with Lombard attributes using principal component analysis (PCA). By shifting the relevant PCA components, we manipulate the style embeddings and incorporate them into our TTS model to generate speech at desired Lombard levels. Evaluations demonstrate that our method preserves naturalness and speaker identity, enhances intelligibility under noise, and provides fine-grained control over prosody, offering a robust solution for controllable Lombard TTS for any speaker.

Problem

• Lombard speech
• text-to-speech
• style control
• speaker adaptation
• speech intelligibility

Innovation

• Lombard speech synthesis
• controllable TTS
• style embeddings
• principal component analysis
• speaker-independent