Bilingual ASR Model for Mixed-Language Assistant Inputs
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Solution Overview
Problem
Existing assistant systems struggle to efficiently process and respond to user inputs in multiple languages, particularly in real-time scenarios, due to the complexity of handling different linguistic structures and the need for accurate translation.
Innovation Solution
A bilingual automatic-speech-recognition (ASR) model is used to receive utterances containing words in multiple languages, generate transcriptions in both languages, and execute tasks based on these transcriptions, enabling real-time responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional separate monolingual ASR models are used for each language, then language-specific accuracy is maintained, but system complexity and processing time increase significantly
Solution Approach 1:
The patent merges multiple monolingual ASR models into a single bilingual ASR model that can process both languages simultaneously. This consolidation reduces the number of separate models from two or more to one, simplifying the system architecture while maintaining the ability to recognize speech in multiple languages with comparable accuracy to specialized monolingual models.
Solution Approach 2:
The bilingual ASR model achieves multi-functionality by being capable of recognizing speech in multiple languages within a single model framework. This universal model replaces the need for language-specific models, allowing the system to handle mixed-language inputs without requiring separate processing pipelines for each language.
2Measurement precision
If multiple separate ASR models are deployed for different languages, then language accuracy is preserved, but real-time processing capability deteriorates
Solution Approach 1:
By combining multiple language models into a single bilingual model, the system eliminates the need to sequentially process audio through multiple separate models. The unified model processes mixed-language speech in a single pass, significantly reducing processing time and enabling real-time performance that would be impossible with sequential monolingual model processing.
3Productivity
If bilingual ASR model is used to process mixed-language inputs, then processing efficiency improves, but model complexity increases
Solution Approach 1:
The bilingual ASR model achieves universality by incorporating the capability to recognize multiple languages within a single model architecture. This multi-functional model processes mixed-language inputs more efficiently than separate models because it handles language identification and transcription in a unified framework, avoiding the overhead of switching between multiple specialized models.
Data Source
AI summary
A method includes receiving, from a client system, one or more utterances comprising one or more first words in a first language and one or more second words in a second language. The method further includes generating, based on a single bilingual automatic-speech-recognition (ASR) model, a transcription of the one or more utterances, such that the transcription comprises one or more first text strings in the first language and one or more second text strings in the second language. The method further includes executing one or more tasks based on the one or more first text strings in the first language and the one or more second text strings in the second language, and sending, to the client system, instructions for presenting a response responsive to the one or more utterances, such that the response is based on both the first and second languages.