Foreign Language Detection via Word Error Rate Estimates
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Solution Overview
Problem
Automatic speech recognition systems face challenges in estimating word error rates without human-generated transcripts and in detecting foreign languages, leading to low quality transcripts and inappropriate language recognition.
Innovation Solution
The system uses multiple automatic speech recognition engines to generate word error rate estimates for audio datasets, which are then analyzed to determine if the language matches the engine's configuration, employing statistical models and machine learning methods to identify foreign languages by extracting features from word error rate estimates and using these features to train a foreign language detector model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple automatic speech recognition engines are used to generate word error rate estimates, then the accuracy of word error rate estimation is improved, but the device complexity increases
Solution Approach 1:
The system segments the speech recognition task by using multiple specialized automatic speech recognition engines, each configured for a particular language. This segmentation allows each engine to focus on its specific language domain, improving the accuracy of word error rate estimates for that language while maintaining manageable complexity through modular architecture.
Solution Approach 2:
A language detection engine acts as an intermediary component that receives audio data, determines the language, and routes it to the appropriate automatic speech recognition engine. This intermediary layer simplifies the overall system by providing a clear decision-making structure that prevents the complexity from escalating, while still enabling accurate word error rate estimation through specialized engines.
2Measurement precision
If automatic speech recognition engines are configured for a particular language, then the recognition accuracy for that language is improved, but the ability to handle foreign languages deteriorates
Solution Approach 1:
The system achieves universality by combining multiple automatic speech recognition engines, each specialized in a particular language, under a unified architecture managed by the language detection engine. This multi-functional approach allows the system to maintain high recognition accuracy for each specific language while simultaneously gaining the versatility to handle multiple languages through the detection and routing mechanism.
Solution Approach 2:
The system dynamically adapts to different languages by using the language detection engine to identify the input language and then activating the corresponding specialized automatic speech recognition engine. This dynamic configuration allows the system to optimize recognition accuracy for the detected language while maintaining the capability to handle various languages, thus balancing specialization with adaptability.
3Measurement precision
If word error rate estimates are used to detect foreign languages, then the foreign language detection accuracy is improved, but the loss of time for processing increases
Solution Approach 1:
The language detection engine performs preliminary action by detecting the language of the audio data before it is processed by the automatic speech recognition engines. By determining the language in advance, the system can route the audio to the appropriate specialized engine, improving foreign language detection accuracy while minimizing processing time through efficient pre-processing and routing decisions.
Data Source
AI summary
A plurality of audio datasets associated with captured audio are provided to a plurality of automatic speech recognition engines, wherein each of the automatic speech recognition engines is configured to recognize speech of a first language. Word error rate estimates that comprise at least one word error rate estimate for each of the plurality of audio datasets are determined from outputs of the plurality of automatic speech recognition engines. From the word error rate estimates, audio in the plurality of audio datasets is determined to include speech in a second language.