Secondary Language Proficiency Detection for Adaptive Speech Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users interacting with automated assistants face challenges in having their secondary language proficiency accurately recognized and resources tailored to their proficiency level, leading to inefficient and inaccurate interactions.
Innovation Solution
A system determines a secondary language proficiency measure based on past user interactions, adjusting settings of language learning applications and speech recognition models to provide tailored interactions and accurate recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an automatic speech recognition model is configured to recognize utterances in the primary language of the user, then recognition accuracy for primary language is improved, but recognition capability for secondary language deteriorates
Solution Approach 1:
The system performs preliminary analysis of user interactions to detect secondary language usage patterns before formal language learning resources are provided. This early detection enables the system to proactively adapt speech recognition models and provide targeted language learning content, resolving the contradiction by preparing the system in advance for multi-language recognition needs.
Solution Approach 2:
The speech recognition model dynamically adjusts its language configuration based on detected user proficiency levels and interaction patterns. The system transitions from a static primary-language-only configuration to a dynamic multi-language configuration, allowing the model to optimize recognition accuracy for each language context while maintaining versatility across multiple languages.
2Adaptability or versatility
If language learning resources are provided without considering user proficiency level, then resource availability is improved, but resource utilization effectiveness deteriorates
Solution Approach 1:
The system applies local quality by providing language learning resources tailored to the user's specific proficiency level in each secondary language. Instead of offering uniform resources, the system analyzes interaction patterns to determine appropriate complexity levels, vocabulary ranges, and grammatical concepts, ensuring resources match the user's current capabilities and promote effective learning.
Solution Approach 2:
The system changes parameters such as vocabulary complexity, sentence structure, and topic difficulty based on the user's detected proficiency level. By dynamically adjusting these resource parameters according to interaction history and language exposure, the system ensures resources are neither too difficult nor too easy, optimizing language learning efficiency while maintaining broad resource availability.
3Adaptability or versatility
If speech recognition model processes all possible secondary language terms, then language coverage is improved, but computational efficiency deteriorates
Solution Approach 1:
The system extracts and processes only the specific secondary language terms and phrases that are relevant to the user's detected interests and proficiency level. By filtering out unnecessary language data and focusing computation on high-probability vocabulary based on interaction patterns, the system maintains comprehensive language coverage for detected languages while significantly reducing computational waste from processing irrelevant terms.
Data Source
Figure 1
Figure 2
Figure 3A~3B
AI summary
Implementations relate to determining a secondary language proficiency measure, for a user in a secondary language (i.e., a language other than a primary language specified for the user), where determining the secondary language proficiency measure is based on past interactions of the user that are related to the secondary language. Those implementations further relate to utilizing the determined secondary language proficiency measure to increase efficiency of user interaction(s), such as interaction(s) with a language learning application and/or an automated assistant. Some of those implementations utilize the secondary language proficiency measure in automatically setting value(s), biasing automatic speech recognition, and/or determining how to render natural language output.