Source-Dependent Text-to-Speech Voice Matching
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Solution Overview
Problem
Text-to-speech systems currently produce generic voices, making it difficult to determine the source of a text message, as they do not associate the voice with the particular person sending the message.
Innovation Solution
A source-dependent text-to-speech system that determines a speech feature vector for a voice associated with a text message and compares it to speaker models to select a preferred match, generating speech based on the selected speaker model, allowing the system to reproduce speech with greater fidelity to the original voice.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a generic text-to-speech system is used, then the system is simple and easy to operate, but the voice produced does not match the source of the message
Solution Approach 1:
The system performs preliminary voice analysis by extracting speech feature vectors from the source's voice sample before text-to-speech conversion. Speaker models are pre-computed and stored for comparison, enabling the system to match the source's voice characteristics in advance rather than using generic voices.
Solution Approach 2:
The system changes the voice parameters by comparing speech feature vectors (such as pitch, timbre, and spectral characteristics) from the source's voice sample against stored speaker models. This parameter matching enables the TTS system to generate voices that closely resemble the original speaker's vocal characteristics.
2Measurement precision
If speaker model comparison is performed, then voice fidelity is improved, but processing time increases
Solution Approach 1:
Speaker models are pre-computed and stored during an offline phase, containing extracted voice characteristics from multiple speakers. This preliminary preparation eliminates the need for real-time voice analysis during message conversion, reducing processing time while maintaining high voice fidelity through accurate model matching.
Solution Approach 2:
Instead of analyzing the source's voice in real-time during message conversion, the system creates and stores copies of voice characteristics in the form of speaker models. These models are then reused for multiple messages from the same speaker, significantly reducing processing time while preserving voice fidelity.
3Measurement precision
If source-dependent voice matching is implemented, then message source recognition is improved, but system interoperability becomes more difficult
Solution Approach 1:
The system uses universal speech feature extraction methods and standard speaker modeling techniques that can be applied across different TTS systems. By relying on common voice characteristics (pitch, timbre, spectral features) rather than system-specific implementations, the system achieves source-dependent matching while maintaining interoperability with other TTS platforms.
Solution Approach 2:
The system transforms voice characteristics into standardized parameter representations (speech feature vectors) that can be compared across different systems. This parameter standardization enables source recognition accuracy while facilitating interoperability, as the same voice matching approach can be implemented across diverse TTS platforms using common acoustic parameters.
Data Source
AI summary
A method of generating speech from text messages includes determining a speech feature vector for a voice associated with a source of a text message, and comparing the speech feature vector to speaker models. The method also includes selecting one of the speaker models as a preferred match for the voice based on the comparison, and generating speech from the text message based on the selected speaker model.