LLM Natural Language and Prosody Generation
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Solution Overview
Problem
Existing speech processing systems struggle to generate natural language responses with contextually relevant prosody information, leading to inconsistent and less engaging user interactions.
Innovation Solution
The system employs large language models (LLMs) to generate natural language responses and corresponding prosody information, utilizing contextual data such as user preferences, environmental signals, and interaction history to determine the appropriate voice characteristics for the synthetic voice.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing speech processing systems generate natural language responses without contextual prosody information, then the system complexity is reduced, but the user engagement and naturalness of interaction deteriorates
Solution Approach 1:
The patent merges natural language generation with prosody information generation by using a single large language model to produce both the text response and the prosody characteristics (pitch, speed, stress) in an integrated manner. This combination allows the system to generate more natural and engaging speech without requiring separate complex modules for prosody generation, thus improving user engagement while managing system complexity through unified processing.
2Reliability
If the system uses large language models to generate contextually relevant prosody information, then the naturalness of speech output is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing by extracting and analyzing contextual information (user profile, environmental signals, interaction history) before generating the response. This preliminary action allows the large language model to receive pre-processed contextual data, enabling more efficient generation of contextually relevant prosody information without requiring the model to process all context during the main generation task, thus reducing overall processing time while maintaining high contextual relevance.
3Adaptability or versatility
If the system incorporates multiple types of contextual data for prosody generation, then the adaptability of voice characteristics is improved, but the difficulty of processing and integrating data increases
Solution Approach 1:
The patent introduces an intermediary processing layer that consolidates multiple types of contextual data (user profile, environmental signals, interaction history) into a unified representation before feeding it to the large language model. This intermediary layer simplifies the data integration process by transforming diverse data formats into a consistent structure, enabling the system to handle multiple data types for adaptable voice characteristics while reducing the complexity of processing and integrating these diverse data sources.
Data Source
AI summary
Techniques for using a language model (e.g., a large language model (LLM)) to generate a natural language response to a user input and prosody information (e.g., voice characteristics associated with a synthetic voice to output the natural language response to the user) are described. The prosody information may correspond to a natural language (e.g., text or tokenized) description, a spectrogram, and/or a latent representation of the voice characteristic(s) associated with the natural language response. In some embodiments, the natural language response and the prosody information may be generated by different portions of layers of the language model. In such embodiments, the output of the layer(s) of the language model configured to generate the natural language response may be provided to the layer(s) of the language model configured to generate the prosody information and the output may be used to generate the prosody information, and vice versa.