Automated Accent Recognition and Speech Scoring in Language Learning
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Solution Overview
Problem
Current language learning systems lack efficient methods for automated generation of speech sample asset production scores and accent recognition, which hampers personalized feedback and speech recognition accuracy for non-native speakers.
Innovation Solution
A supervised machine learning module is trained using production speech samples and user background information to generate automated production scores and recognize accents, enabling improved speech recognition by selecting appropriate acoustic models based on accent type and strength.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual perception exercises are used to generate production scores, then score accuracy may be maintained, but system productivity and automation level deteriorate
Solution Approach 1:
The system enables users to automatically generate production scores for their own speech samples through the trained machine learning module, eliminating the need for manual perception exercises by other users. The module processes speech samples and generates scores autonomously based on the trained model.
Solution Approach 2:
The patent replaces the manual mechanical process of human perception exercises with an automated machine learning system. The trained supervised machine learning module substitutes human listeners, providing automated score generation while maintaining consistency and eliminating manual labor requirements.
2Productivity
If automated scoring is implemented without accent recognition, then processing speed improves, but speech recognition accuracy for non-native speakers deteriorates
Solution Approach 1:
The system applies different acoustic models based on the detected accent type. Instead of using a single generic model, the patent identifies the specific accent characteristics of non-native speakers and selects or adapts appropriate acoustic models tailored to each accent type, thereby improving recognition accuracy for diverse speakers.
Solution Approach 2:
The machine learning module detects and quantifies accent parameters from speech samples. By analyzing acoustic features and identifying accent characteristics, the system changes the speech recognition parameters dynamically based on the detected accent type, optimizing recognition performance for each speaker.
3Reliability
If personalized feedback is provided for each user, then learning effectiveness improves, but system complexity and resource requirements deteriorate
Solution Approach 1:
The system automatically generates personalized feedback by comparing each user's speech samples against native speaker references using the trained machine learning module. Users receive individualized production scores and accent analysis without requiring manual intervention, achieving personalization through automated processing.
Solution Approach 2:
The system uses native speaker speech samples as reference models for comparison. By creating idealized copies of native pronunciation and comparing user speech against these references, the system generates personalized feedback without requiring complex individualized modeling for each user.
Data Source
AI summary
Methods for automated generation of speech sample asset production scores for users of a distributed language learning system, automated accent recognition and quantification and improved speech recognition. Utilizing a trained supervised machine learning module which is trained utilizing a training set comprising a plurality of production speech sample asset recordings, associated production scores generated by system users performing perception exercises and user background information. The trained supervised machine learning module may be configured for automated accent recognition, by feeding a candidate production speech sample asset so as to automate the generation of a speech sample asset production score and user background information. As such, the user background information may be translated into an accent type categorization and the speech sample asset production score may be translated into an accent strength. In further embodiments, the accent type categorization generated using the trained system may be utilized for improved speech recognition.