Synthesized Speech Generation for Impaired-Input Conversations
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Solution Overview
Problem
Voice-based user interfaces struggle to effectively generate synthesized speech that accurately reflects user input, particularly for users with speech impairments or in adverse conditions, leading to communication challenges.
Innovation Solution
A system that generates synthesized speech audio data based on user interface inputs, using tailored speech recognition models and dynamic prosodic properties to enhance speech synthesis, ensuring clarity and comprehension by additional participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition is used to process spoken input, then text can be generated accurately, but the spoken input may contain characteristics (speech impairment, background noise) that prevent additional participants from understanding it
Solution Approach 1:
The system creates a synthesized speech copy of the recognized text that mimics the original speaker's voice characteristics without reproducing the impairments or noise. This copy is then presented to additional participants, allowing them to understand the message clearly while the original speaker's intent and identity are preserved
Solution Approach 2:
The system introduces an intermediary processing layer that takes the recognized text and generates synthesized speech as a mediator between the original spoken input and the additional participants. This intermediary transforms the information into a form that is both accurate to the original meaning and comprehensible to all participants
2Measurement precision
If a tailored speech recognition model is used for speech-impaired users, then recognition accuracy improves, but system complexity and resource usage increase
Solution Approach 1:
The system performs preliminary training of tailored speech recognition models during an initialization phase, storing the trained models for later use. This allows the system to be prepared for speech-impaired users before actual conversation begins, avoiding the need to perform complex training operations in real-time
Solution Approach 2:
The system dynamically selects between different speech recognition models based on the user's needs - using standard models for typical users and switching to tailored models for speech-impaired users. This dynamic adaptation allows the system to optimize resource usage while maintaining high accuracy when needed
3Loss of information
If synthesized speech is generated to overcome spoken input limitations, then communication clarity improves, but processing time and computational resources increase
Solution Approach 1:
The system prepares synthesized speech in advance by generating it as soon as the recognized text is available, before the conversation flow requires it. This preliminary generation allows the synthesized speech to be ready for immediate playback, minimizing the perceived delay to users
Solution Approach 2:
The system maintains continuous operation of the speech synthesis process, keeping the synthesis engine active and ready during the conversation. This continuous operation avoids the need to start and stop synthesis operations, reducing overhead and processing delays while maintaining clear communication
Data Source
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AI summary
Generating synthesized speech audio data on behalf of a given user in a conversation. The synthesized speech audio data includes synthesized speech that incorporates textual segment(s). The textual segment(s) can include recognized text that results from processing spoken input, of the given user, using a speech recognition model and/or can include a selection of a rendered suggestion that conveys the textual segment(s). Some implementations dynamically determine one or more prosodic properties for use in speech synthesis of the textual segment, and generate the synthesized speech with the one or more determined prosodic properties. The prosodic properties can be determined based on the textual segment(s) used in speech synthesis, textual segment(s) corresponding to recent spoken input of additional participant(s), attribute(s) of relationship(s) between the given user and additional participant(s) in the conversation, and/or feature(s) of a current location for the conversation.