Prosody Prediction Embeddings for Consistent Speech Synthesis
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Solution Overview
Problem
Existing speech-processing systems struggle to accurately predict and apply prosody to synthesized speech, particularly in distributed systems with varying computational resources, leading to inconsistencies and suboptimal audio output.
Innovation Solution
A speech-synthesis component that includes a phoneme encoder, prosody prediction component, and speech decoder, trained using text and audio data, to determine phoneme and prosody embeddings, enabling precise prosody prediction and synthesis across user devices and remote systems, leveraging both local and remote computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prosody prediction is performed using complex models to improve accuracy, then prosody prediction accuracy is improved, but computational resource requirements increase
Solution Approach 1:
The system segments the speech processing task into distinct components: phoneme encoding, prosody prediction, and speech decoding. The prosody prediction component is further divided into duration prediction and pitch prediction sub-components. This segmentation allows each component to be optimized independently, enabling accurate prosody prediction while managing computational resources efficiently through selective processing.
Solution Approach 2:
The system applies different processing qualities to different parts of the speech signal based on local requirements. The prosody prediction component processes specific phonemes with higher accuracy where needed, while using simplified processing for other segments. This local quality approach maintains overall prosody accuracy without requiring maximum computational resources for every aspect of speech processing.
2Device complexity
If distributed computing is used to reduce device complexity, then device complexity is reduced, but system consistency deteriorates
Solution Approach 1:
The system introduces an intermediary training phase where both user devices and remote systems are trained on identical speech data and prosody targets. This intermediary training process ensures that all systems learn the same prosody patterns and prediction rules, creating consistency across distributed devices. The trained models are then deployed to various devices, maintaining prosody composition stability while allowing distributed execution.
Solution Approach 2:
The system changes the parameters of the speech processing model through training, adjusting weights and thresholds to optimize prosody prediction. By training models with specific parameter configurations on standardized data, the system ensures consistent prosody output across different devices. The trained parameters are transferred to distributed systems, maintaining consistency while reducing local computational complexity.
Data Source
AI summary
A speech-processing system receives input data representing text. A prosody prediction component processes the input data to determine prosody embedding data corresponding to prosody of the text. A decoder processes the prosody embedding data and phoneme encoded data derived from the input data to determine audio output data corresponding to the text and the prosody.