Automated Language Detection for Speech Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current speech recognition engines often fail to handle multiple languages effectively, leading to unusable automated assistants and excessive resource usage when encountering utterances in unsupported languages, and require manual language designation, which can result in errors and resource inefficiency.
Innovation Solution
Implementing a system that automatically determines the language for speech recognition using user profiles and probability metrics based on past usage and contextual parameters, selecting only the most likely languages for processing to conserve resources and improve accuracy.
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
Solution Approach 1:
The speech recognition system is segmented into multiple independent language-specific models (e.g., English model, Spanish model, French model). Each model is trained separately for a specific language, allowing the system to handle multiple languages while keeping each individual model relatively simple and manageable.
Solution Approach 2:
The automated assistant system is designed with multi-functionality to support multiple languages through a single unified architecture that can dynamically select and switch between different language-specific speech recognition models based on the detected language of the incoming utterance.
2Measurement precision
If all available speech recognition models are processed for every spoken utterance, then language identification accuracy is improved, but computational resource usage increases
Solution Approach 1:
The system performs preliminary language identification using acoustic models and probability metrics before committing to full speech recognition processing. By pre-assessing the likelihood of different languages based on phoneme-level analysis, the system can prepare the appropriate language-specific model in advance, improving accuracy while avoiding the need to process all models simultaneously.
Solution Approach 2:
The system applies partial processing by using acoustic models to generate probability metrics for multiple languages, then selectively applying full speech recognition only to the most likely candidate languages rather than processing all available language models, thus reducing computational overhead while maintaining sufficient accuracy.
3Measurement precision
If a user must manually designate the default language, then language recognition accuracy is improved, but ease of operation deteriorates due to additional user interaction requirements
Solution Approach 1:
The system automatically determines the language of spoken utterances using acoustic models and probability metrics without requiring manual user designation. The automated language identification process analyzes phoneme characteristics and compares them against multiple language models to autonomously select the correct language, eliminating the need for user intervention while maintaining recognition accuracy.
Solution Approach 2:
The system uses feedback from acoustic model analysis and probability metric calculations to automatically adjust and determine the correct language for speech recognition, creating a closed-loop system that continuously refines language identification based on the characteristics of the incoming utterance without requiring explicit user input.
4Device complexity
If speech recognition in a single language is implemented, then device complexity is reduced, but adaptability deteriorates when users speak in unsupported languages
Solution Approach 1:
The speech recognition capability is segmented into separate language-specific models that can be independently managed. This allows the system to maintain simple, focused models for each language while providing the overall system with the ability to support multiple languages through selective model activation based on the detected language of the utterance.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Determining a language for speech recognition of a spoken utterance received via an automated assistant interface for interacting with an automated assistant. Implementations can enable multilingual interaction with the automated assistant, without necessitating a user explicitly designate a language to be utilized for each interaction. Implementations determine a user profile that corresponds to audio data that captures a spoken utterance, and utilize language(s), and optionally corresponding probabilities, assigned to the user profile in determining a language for speech recognition of the spoken utterance. Some implementations select only a subset of languages, assigned to the user profile, to utilize in speech recognition of a given spoken utterance of the user. Some implementations perform speech recognition in each of multiple languages assigned to the user profile, and utilize criteria to select only one of the speech recognitions as appropriate for generating and providing content that is responsive to the spoken utterance.