Speech Quality Indicator Generation for Natural Interaction
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Solution Overview
Problem
Current speech processing systems fail to consistently match the quality of user speech in output responses, leading to an unnatural user experience, as they often ignore or misinterpret speech quality indicators.
Innovation Solution
A system that generates and adjusts speech quality indicators for output content based on user speech characteristics, ensuring that responses conform to the intended quality, such as whisper or shout, by processing audio data to determine speech qualities and using machine learning models to classify speech as whispered or normal, and then applying these indicators to text-to-speech processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech quality indicators are generated and applied to output content, then the naturalness of user interaction is improved, but the system complexity increases
Solution Approach 1:
The system performs preliminary classification of user speech quality (whispered vs. normal) before generating the response. By determining the speech quality indicator in advance and passing it to the speechlet component, the system ensures that the output content automatically conforms to the appropriate quality without requiring complex real-time adjustments during response generation.
Solution Approach 2:
The speech quality indicator serves as an intermediary parameter that bridges the user's input speech and the system's output response. This indicator is generated by the speech processing system and consumed by the speechlet component, enabling quality-matched responses without requiring direct complex interaction between the speech recognition and content generation modules.
2Measurement precision
If speech quality classification is performed using machine learning models, then the accuracy of speech quality detection is improved, but the processing time increases
Solution Approach 1:
The system extracts only the essential speech quality characteristic (whether the speech is whispered or normal) from the audio input, rather than performing comprehensive analysis of all speech parameters. This extraction approach uses machine learning models to identify the key quality indicator needed for response generation, minimizing processing time while maintaining detection accuracy.
Data Source
AI summary
Techniques for ensuring content output to a user conforms to a quality of the user's speech, even when a speechlet or skill ignores the speech's quality, are described. When a system receives speech, the system determines an indicator of the speech's quality (e.g., whispered, shouted, fast, slow, etc.) and persists the indicator in memory. When the system receives output content from a speechlet or skill, the system checks whether the output content is in conformity with the speech quality indicator. If the content conforms to the speech quality indicator, the system may cause the content to be output to the user without further manipulation. But, if the content does not conform to the speech quality indicator, the system may manipulate the content to render it in conformity with the speech quality indicator and output the manipulated content to the user.