Automated Speech Proficiency Evaluation Using Suprasegmental Analysis
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Solution Overview
Problem
Current systems for evaluating language proficiency in speech rely heavily on human evaluators, which are costly, time-consuming, and prone to inconsistency, and struggle with accurately assessing unconstrained speech due to its irregular nature.
Innovation Solution
An automated system using a microphone and computing device to process audio signals, recognizing phones and pauses, dividing them into tone units, identifying prominent syllables and tone choices, and calculating suprasegmental parameters to determine language proficiency, leveraging machine learning classifiers and speech corpora for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human evaluators are used to assess language proficiency, then the evaluation can be performed with current technology, but the process is costly, time-consuming, and prone to inconsistency
Solution Approach 1:
The patent replaces the mechanical system of human evaluators with an automated computational system. The system uses automatic speech recognition to transcribe speech, extracts suprasegmental features (stress, pitch, rhythm), and applies machine learning models to assess proficiency, eliminating the need for human raters and achieving consistent, scalable evaluation.
Solution Approach 2:
The system performs self-service by automatically processing speech evaluations without requiring external human intervention. The automated pipeline handles the entire evaluation process from speech input to proficiency scoring, making the system independent and scalable.
2Measurement precision
If human evaluators assess unconstrained speech, then the evaluation can be performed, but accuracy is reduced due to the irregular nature of unconstrained speech
Solution Approach 1:
The patent segments the evaluation process into distinct analytical stages: speech transcription, suprasegmental feature extraction (stress, pitch, rhythm), and proficiency assessment. This segmentation allows the system to handle the complexity of unconstrained speech through systematic decomposition into manageable analysis components.
Solution Approach 2:
The system changes the evaluation parameters from segmental accuracy (word-level recognition) to suprasegmental features (stress, pitch, rhythm patterns). These parameter changes enable the system to assess proficiency based on prosodic characteristics that remain stable even in unconstrained speech, improving measurement precision.
3Productivity
If automated systems are used to provide quick and consistent ratings, then evaluation efficiency improves, but the system complexity increases
Solution Approach 1:
The system achieves multi-functionality by integrating multiple capabilities into a single unified platform: automatic speech recognition, suprasegmental feature extraction, machine learning-based proficiency assessment, and feedback generation. This universal system handles diverse evaluation tasks efficiently while managing complexity through integrated design.
Data Source
AI summary
Systems and methods for evaluating human speech. Implementations may include: a microphone coupled with a computing device comprising a microprocessor, a memory, and a display operatively coupled together. The microphone may be configured to receive an audible unconstrained speech utterance from a user whose proficiency in a language is being tested and provide a corresponding audio signal to the computing device. The microprocessor and memory may receive the audio signal and process the audio signal by recognizing a plurality of phones and a plurality of pauses and calculate a plurality of suprasegmental parameters using the plurality of pauses and the plurality of phones. The microprocessor and memory may use the plurality of suprasegmental parameters to calculate a language proficiency rating for the user and display the language proficiency rating of the user on the display associated with the computing device.