Secondary Language Proficiency for Adaptive Speech Recognition

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Solution Overview

Problem

Users face challenges in interacting with automated assistants and language learning applications due to the inability to recognize secondary languages and the lack of tailored resources for their proficiency level, leading to inefficient and inaccurate interactions.

Innovation Solution

A secondary language proficiency measure is determined based on past user interactions, allowing for automatic setting of adjustable settings in language learning applications and biasing speech recognition models to improve accuracy and efficiency in language learning and interaction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If automatic speech recognition model is configured to recognize only primary language, then recognition accuracy for primary language is improved, but ability to recognize secondary language deteriorates

Engineering Contradiction:

Improverecognition accuracyVSAvoidlanguage recognition capability

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The speech recognition model dynamically adapts its language configuration based on detected user intent. When a user indicates interest in a secondary language, the system transitions from recognizing only the primary language to also recognizing the secondary language, allowing the model to change its operational state based on contextual needs

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the language parameter of the speech recognition model from primary language only to include secondary language based on user interest detection. This parameter modification enables the model to recognize utterances in the secondary language while maintaining primary language capability

Inventive Principle:

Principle #35Parameter changes

2Productivity

If automated assistant attempts to interpret secondary language utterances as primary language terms, then processing capability is maintained, but interpretation accuracy deteriorates

Engineering Contradiction:

Improveprocessing capabilityVSAvoidinterpretation accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system introduces language interest detection as an intermediary mechanism between the user's utterance and the automated assistant's interpretation. This intermediary detects the user's interest in secondary languages and mediates the processing by configuring the speech recognition model accordingly, preventing misinterpretation of secondary language terms

Inventive Principle:

Principle #24Intermediary (Mediator)

3Quantity of substance

If language learning resources are provided without considering user proficiency level, then resource availability is improved, but usability deteriorates

Engineering Contradiction:

Improveresource availabilityVSAvoidusability

Core Design Contradiction:

Quantity of substanceVSEase of operation

Solution Approach 1:

The system automatically determines the user's language proficiency level by analyzing past interactions and uses this information to self-configure the language learning resources. The user does not need to manually indicate their proficiency level, as the system serves itself by adapting resources based on detected proficiency

Inventive Principle:

Principle #25Self-service

4Ease of operation

If manual setting of language learning parameters is required, then user control is improved, but interaction time increases

Engineering Contradiction:

Improveuser controlVSAvoidinteraction time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system performs preliminary action by automatically determining language proficiency level and configuring learning parameters before the user needs to access language learning resources. This eliminates the need for users to manually set these parameters during initial interactions

Inventive Principle:

Principle #10Preliminary action

5Ease of manufacture

If language learning application uses inaccurate proficiency settings, then setup simplicity is improved, but computational efficiency deteriorates

Engineering Contradiction:

Improvesetup simplicityVSAvoidcomputational efficiency

Core Design Contradiction:

Ease of manufactureVSLoss of energy

Solution Approach 1:

The system continuously monitors user interactions and uses this feedback to refine and update the language proficiency level determination. This feedback mechanism ensures that the proficiency settings remain accurate over time, preventing computational waste from using inappropriate resources while maintaining simple setup

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260004777A1Determining and utilizing secondary language proficiency measure

Publication Date: 2026.01.01 GOOGLE LLC

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.