Prosody Models for Natural Speech Synthesis
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Solution Overview
Problem
Text-to-speech synthesis systems lack natural variability in prosody, resulting in flat and artificial-sounding speech, as they fail to replicate the rich emotional and contextual variations present in human speech.
Innovation Solution
The development of methods and systems for training and applying prosody models that generate and apply natural, varying prosody to speech synthesis, using speech recognition engines to annotate text with prosody information, translating formats as needed, and selecting multiple models based on context and confidence indicators to produce nuanced pitch, rate, and volume changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single prosody style is used in TTS synthesis, then the system is simple to implement, but the speech lacks natural variability and sounds flat and artificial
Solution Approach 1:
The patent segments prosody into multiple independent styles (e.g., newscaster, conversational, emphatic) and trains separate prosody models for each style. This allows the system to select and apply appropriate prosody styles based on context, achieving natural variability without requiring a completely complex unified model. Each style model is trained on specific annotated speech data characteristic of that style.
Solution Approach 2:
The patent creates a universal prosody model framework that can handle multiple prosody styles through a common architecture. The system uses a single text-to-speech engine that can be directed to apply different prosody models, making the system multi-functional while maintaining a unified base structure. This reduces overall complexity compared to having entirely separate systems for each style.
2Adaptability or versatility
If multiple prosody models are applied to a single piece of text, then the speech achieves richer natural variation, but conflicts in prosody directives must be arbitrated
Solution Approach 1:
The patent implements feedback mechanisms where prosody models provide confidence indicators with their directives. The arbitration system uses this feedback to resolve conflicts between multiple models, selecting the prosody directives with highest confidence or those most appropriate for the given context. This systematic feedback-based arbitration reduces the complexity of coordinating multiple models.
Solution Approach 2:
The patent changes the parameter of model selection by using context analysis (keywords, subject matter, desired emotional tone) to dynamically select which prosody models to apply and how to weight their directives. This parameter-based control mechanism simplifies the coordination of multiple models by providing clear rules for their interaction based on the synthesis context.
Data Source
AI summary
Techniques for training and applying prosody models for speech synthesis are provided. A speech recognition engine processes audible speech to produce text annotated with prosody information. A prosody model is trained with this annotated text. After initial training, the model is applied during speech synthesis to generate speech with non-standard prosody from input text. Multiple prosody models can be used to represent different prosody styles.