Language Selection Model for Speech Recognition
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Solution Overview
Problem
Existing speech recognition engines are often limited to recognizing a single language, leading to failures and erroneous outputs when encountering multiple languages, and require manual language designation, which can be resource-intensive and prone to user errors.
Innovation Solution
A system and method utilizing a trained language selection model, specifically a neural network model, to automatically determine the language of a spoken utterance by processing audio data and generating predicted probabilities for each supported language, thereby selecting the appropriate language for speech recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a speech recognition engine is configured to recognize speech in multiple languages, then language coverage is improved, but device complexity increases due to requiring multiple speech recognition models and manual language designation interfaces
Solution Approach 1:
The patent introduces a language selection model as an intermediary component that automatically determines the language of spoken utterances. This mediator sits between the audio input and multiple speech recognition models, selecting the appropriate model based on detected language characteristics, thereby resolving the complexity of managing multiple language models without manual intervention
Solution Approach 2:
The system implements self-service by enabling the speech recognition engine to automatically detect and select the appropriate language model without requiring manual user designation. The language selection model autonomously analyzes audio features and determines the spoken language, eliminating the need for users to manually configure language settings
2Measurement precision
If a speech recognition engine requires manual language designation, then language selection accuracy can be improved, but ease of operation deteriorates due to additional user interaction requirements
Solution Approach 1:
The patent applies preliminary action by pre-training the language selection model on diverse audio data from multiple languages before deployment. This preliminary training enables the model to automatically and accurately identify the language of incoming utterances without requiring real-time manual input from users, thus maintaining accuracy while improving ease of operation
3Adaptability or versatility
If multiple speech recognition models are maintained for different languages, then language recognition capability is improved, but resource consumption increases due to processing multiple models
Solution Approach 1:
The patent extracts only the necessary speech recognition model for the detected language instead of running multiple models simultaneously. The language selection model identifies the spoken language and extracts/activates only the corresponding speech recognition model, thereby maintaining multilingual capability while significantly reducing computational resource consumption
Data Source
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AI summary
Methods and systems for training and/or using a language selection model for use in determining a particular language of a spoken utterance captured in audio data. Features of the audio data can be processed using the trained language selection model to generate a predicted probability for each of N different languages, and a particular language selected based on the generated probabilities. Speech recognition results for the particular language can be utilized responsive to selecting the particular language of the spoken utterance. Many implementations are directed to training the language selection model utilizing tuple losses in lieu of traditional cross-entropy losses. Training the language selection model utilizing the tuple losses can result in more efficient training and/or can result in a more accurate and/or robust model – thereby mitigating erroneous language selections for spoken utterances.