Expressive Speech Translation with Performance-Preserving Dubbing
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Solution Overview
Problem
Existing speech translation systems fail to accurately reproduce the expressive characteristics of source speech, such as intonation, stress, and emotion, when translating speech from one language to another, leading to a loss of intelligibility and entertainment value in synthesized speech.
Innovation Solution
An expressive speech translation system that utilizes machine learning models to disentangle and encode vocal performance characteristics, including intonation, stress, and emotion, while translating speech, ensuring synchronization with the source speech's duration and suppressing noise, to generate high-quality synthesized speech in the target language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If speech translation systems translate speech from one language to another, then language translation is achieved, but expressive characteristics (intonation, stress, emotion) are lost
Solution Approach 1:
The system segments speech into separate components: linguistic content (text) and expressive characteristics (vocal performance features). By disentangling these elements, the system can translate the text while preserving the expressive characteristics through separate encoding and synthesis pathways.
Solution Approach 2:
The system introduces an intermediary representation that separates linguistic meaning from vocal performance. This intermediary structure allows translation operations to act on linguistic content while vocal performance characteristics are maintained as separate attributes that can be reapplied to the translated speech.
2Adaptability or versatility
If speech is translated to a different language, then communication between languages is enabled, but intelligibility and entertainment value decrease
Solution Approach 1:
The system changes the parameters of speech synthesis by independently controlling linguistic parameters (translation) and expressive parameters (vocal performance characteristics). This parameter separation allows the system to maintain high intelligibility and entertainment value in translated speech by preserving the original speaker's expressiveness across language boundaries.
3Manufacturing precision
If vocal performance characteristics are encoded and reproduced, then expressive speech translation is achieved, but system complexity increases
Solution Approach 1:
The system adds a new dimension to speech processing by separating the speech representation into linguistic and expressive dimensions. This dimensional separation allows complex expressive characteristics to be handled as distinct features, making the overall system architecture more manageable despite the increased precision requirements.
Data Source
AI summary
An expressive speech translation system may process source speech in a source language and output synthesized speech in a target language while retaining vocal performance characteristics such as intonation, emphasis, rhythm, style, and/or emotion. The system may receive a transcript of the source speech, translate it, and generate transcript data. To generate the synthesized speech, the system may process the transcript data with a language embedding representing language-dependent speech characteristics of the target language, a speaker embedding representing speaker-dependent voice identity characteristics of a speaker, and a performance embedding representing the vocal performance characteristics of the source speech. The system may control the duration of segments of the synthesized speech to better align with corresponding segments of the source speech for the purpose of dubbing multimedia content with synthesized speech in a language different from that of the original audio.