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Related Experiment Video

Updated: Jul 1, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Attention-based speech feature transfer between speakers.

Hangbok Lee¹, Minjae Cho¹, Hyuk-Yoon Kwon¹

¹Department of Industrial Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea.

Frontiers in Artificial Intelligence

|March 12, 2024

Summary

This summary is machine-generated.

This study introduces a novel speech synthesis method to transfer a source speaker's vocal style to target speaker speech. The technique modifies attention weights in speech synthesis models for style transfer.

Keywords:

attention mechanism feature transfer speech features speech similarity speech synthesis

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Last Updated: Jul 1, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Published on: August 9, 2024

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Published on: September 27, 2024

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Published on: December 6, 2024

Area of Science:

Speech synthesis
Machine learning
Digital signal processing

Background:

Speaker characteristics in speech synthesis are crucial for naturalness.
Existing methods may struggle with accurate style transfer.
Attention mechanisms in deep learning models offer potential for feature extraction.

Purpose of the Study:

To develop an effective method for incorporating source speaker characteristics into target speaker speech.
To enable speech synthesis models to generate target speaker speech with source speaker style.
To explore the role of attention models in capturing and transferring speaker-specific features.

Main Methods:

Focused on the attention model within a speech synthesis framework.
Extracted speaker features including spectrogram, pitch, intensity, formant, pulse, and voice breaks.
Trained separate models for source and target speakers, then replaced source attention weights with target weights.

Main Results:

Successfully generated target speaker speech exhibiting source speaker styles.
Validated model effectiveness using similarity analysis with five evaluation metrics.
Demonstrated practical application through real-world examples.

Conclusions:

The proposed method offers a simple yet effective approach to speaker style transfer in speech synthesis.
Attention weight manipulation is a viable technique for achieving style imitation.
The model shows promise for applications requiring personalized or stylized speech generation.