Text-to-Speech Model Using Vocal Characteristic Encoders
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Solution Overview
Problem
Current text-to-speech (TTS) systems struggle to accurately generate speech with specific vocal characteristics, such as accent, tone, and emotion, often resulting in synthesized speech that lacks naturalness and authenticity.
Innovation Solution
The proposed solution involves a speech model that uses a combination of linguistic and paralinguistic encoders to process text data and vocal characteristic data, generating spectrogram data that captures the desired vocal traits, allowing for the creation of synthesized speech that mimics human speech in terms of accent, tone, and emotion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional text-to-speech systems are used, then the system structure is simple, but the generated speech lacks naturalness and authenticity in vocal characteristics
Solution Approach 1:
The system divides the speech generation task into separate modules: a text encoder that processes linguistic information, a vocal characteristic encoder that processes paralinguistic features, and a synthesis module that combines them. This segmentation allows each component to specialize in specific aspects, improving the accuracy of vocal characteristic generation while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary representation layer that transforms both text and vocal characteristic data into a unified feature space. This intermediary mechanism bridges the linguistic and paralinguistic domains, enabling accurate integration of vocal characteristics into the synthesized speech without requiring direct complex interactions between all system components.
2Manufacturing precision
If detailed vocal characteristic data is processed, then the naturalness of synthesized speech improves, but the processing time increases
Solution Approach 1:
The system performs preliminary processing of vocal characteristic data by encoding it into a compact representation before the main synthesis process. This pre-processing step extracts and prepares the essential paralinguistic features in advance, reducing the computational burden during real-time speech generation while preserving the naturalness of the output.
Solution Approach 2:
The patent transforms the input vocal characteristic data into a transformed feature space through encoding operations. This parameter transformation compresses the information while maintaining the essential characteristics, allowing the system to process detailed vocal data efficiently and reduce processing time without sacrificing speech naturalness.
3Reliability
If multiple encoders are used to process text and vocal characteristics, then the quality of synthesized speech improves, but the device complexity increases
Solution Approach 1:
The patent designs encoders that can process multiple types of input data through a unified architecture. The text encoder and vocal characteristic encoder share common computational resources and follow similar processing patterns, allowing the system to maintain high speech quality through multiple processing streams while reducing overall device complexity through code reusability and architectural efficiency.
Data Source
AI summary
During text-to-speech processing, a speech model creates synthesized speech that corresponds to input data. The speech model may include an encoder for encoding the input data into a context vector and a decoder for decoding the context vector into spectrogram data. The speech model may further include a voice decoder that receives vocal characteristic data representing a desired vocal characteristic of synthesized speech. The voice decoder may process the vocal characteristic data to determine configuration data, such as weights, for use by the speech decoder.