Automated Assistant Language Detection via User Profile Segmentation
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Solution Overview
Problem
Existing speech recognition engines often fail to handle multiple languages effectively, leading to unusable automated assistants and excessive resource usage when encountering unsupported languages, and require manual language selection, which can result in errors and resource inefficiency.
Innovation Solution
Implementing a system that automatically determines the language for speech recognition using user profiles and probability metrics based on contextual parameters, selecting only the most likely languages to reduce resource usage and improve response accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a speech recognition engine is configured to recognize speech in multiple languages, then language coverage is improved, but device complexity and resource usage increase
Solution Approach 1:
The system segments the language recognition task by first using an acoustic model to detect invocation phrases and determine user profiles, then selecting specific language models based on contextual parameters. This divides the complex multi-language recognition into manageable stages: invocation detection, user identification, context analysis, and language model selection.
Solution Approach 2:
The system performs preliminary actions by pre-configuring user profiles with associated language models and contextual parameters before actual speech recognition occurs. When a user invokes the assistant, the system has already prepared the appropriate language models based on historical data and user preferences, enabling rapid selection without real-time complexity.
2Reliability
If a user manually designates a default language for speech recognition, then speech recognition accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system provides self-service by automatically determining the appropriate language model based on user profiles and contextual parameters without requiring manual user intervention. The system monitors user behavior, analyzes contextual data, and autonomously selects the correct language model, making the interaction seamless and convenient while maintaining high accuracy.
Solution Approach 2:
The system uses feedback from user interactions to dynamically adjust language model selection. By monitoring user profiles, historical data, and contextual parameters, the system continuously learns and adapts to user language preferences, improving accuracy over time without requiring explicit user reconfiguration.
3Device complexity
If a single language speech recognition engine is used, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The system achieves universality by creating a framework that can handle multiple languages through a single unified architecture. The acoustic model serves multiple functions by detecting invocation phrases across different languages, user profiles store multiple language associations, and contextual parameters enable dynamic language model selection, allowing one system to perform what would traditionally require multiple separate systems.
Data Source
AI summary
Determining a language for speech recognition of a spoken utterance received via an automated assistant interface for interacting with an automated assistant. Implementations can enable multilingual interaction with the automated assistant, without necessitating a user explicitly designate a language to be utilized for each interaction. Implementations determine a user profile that corresponds to audio data that captures a spoken utterance, and utilize language(s), and optionally corresponding probabilities, assigned to the user profile in determining a language for speech recognition of the spoken utterance. Some implementations select only a subset of languages, assigned to the user profile, to utilize in speech recognition of a given spoken utterance of the user. Some implementations perform speech recognition in each of multiple languages assigned to the user profile, and utilize criteria to select only one of the speech recognitions as appropriate for generating and providing content that is responsive to the spoken utterance.