Speech Recognition Parameter Adjustment for User Experience
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Solution Overview
Problem
Existing speech recognition systems fail to provide personalized user experiences, as they do not effectively tailor the speed versus accuracy tradeoff based on individual user experience levels, leading to suboptimal performance for novice and experienced users.
Innovation Solution
A cloud-based speech recognition service classifies users as novice or experienced based on usage metrics and adjusts settings such as beam pruning, language models, and acoustic models to prioritize accuracy for novices and speed for experienced users, using a binary classifier and user-specific training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition settings are optimized for maximum accuracy using larger language models and adjusted parameters, then recognition accuracy improves for novice users, but processing speed and latency increase
Solution Approach 1:
The system dynamically adjusts speech recognition parameters based on user classification. Novice users receive settings optimized for accuracy (larger language models, adjusted beam pruning), while experienced users receive settings optimized for speed. This dynamic parameter adjustment resolves the contradiction by making the system adaptive to different user needs rather than using fixed settings for all users.
Solution Approach 2:
Different speech recognition parameter configurations are applied to different user groups. Novice users receive a specific configuration (larger language model, adjusted beam pruning parameter) while experienced users receive a different configuration. This local differentiation allows each user group to receive the quality settings appropriate to their needs, resolving the accuracy-speed tradeoff.
2Productivity
If speech recognition parameters are adjusted to favor speed over accuracy, then processing latency decreases for experienced users, but recognition accuracy deteriorates
Solution Approach 1:
The system dynamically switches between accuracy-optimized and speed-optimized parameter settings based on user experience level. Experienced users are automatically assigned parameters that favor speed (smaller language models, different beam pruning settings), while novice users receive accuracy-optimized settings. This dynamic adaptation resolves the contradiction by matching parameters to user needs.
Solution Approach 2:
Different parameter configurations are locally applied to different user segments. Experienced users receive a specific parameter set optimized for speed, while novice users receive a different parameter set optimized for accuracy. This localized parameter adjustment allows the system to optimize for productivity where appropriate without sacrificing accuracy for users who need it.
3Ease of operation
If a single default speech recognition setting is used for all users, then system complexity is reduced and ease of operation is improved, but user experience is not personalized and performance is suboptimal for specific user groups
Solution Approach 1:
The system automatically classifies users as novice or experienced based on their usage patterns and automatically applies the appropriate parameter configuration without requiring user intervention. Users simply use the service and the system self-adjusts the recognition parameters based on their behavior, maintaining ease of operation while achieving personalization.
Solution Approach 2:
The system changes speech recognition parameters (language model size, beam pruning settings) based on user classification. By automatically detecting user experience level and adjusting parameters accordingly, the system achieves adaptability without requiring users to manually configure settings, thus maintaining simplicity while enabling personalization.
Data Source
AI summary
Audio data that encodes an utterance of a user is received. It is determined that the user has been classified as a novice user of a speech recognizer. A speech recognizer setting is selected that is used by the speech recognizer in generating a transcription of the utterance. The selected speech recognizer setting is different than a default speech recognizer setting that is used by the speech recognizer in generating transcriptions of utterances of users that are not classified as novice users. The selected speech recognizer setting results in increased speech recognition accuracy in comparison with the default setting. A transcription of the utterance is obtained that is generated by the speech recognizer using the selected setting.