Multilingual E2E Speech Recognition with Language-Specific Adapters
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Solution Overview
Problem
Conventional multilingual speech recognition systems face challenges in achieving low word error rates and latency constraints due to imbalanced training data across languages, with existing end-to-end models often relying on separate language models and lexicons, which are not efficient for real-world applications.
Innovation Solution
A multilingual end-to-end model is developed that conditions language-specific adapter layers on a language vector, allowing for training on diverse data and incorporating language-specific adapter modules to fine-tune the model for each language, maintaining parameter efficiency and improving performance across languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single multilingual acoustic model is used to support multiple languages, then device complexity is reduced, but word error rate increases due to imbalanced training data across languages
Solution Approach 1:
The patent segments the monolithic multilingual model into a shared acoustic model backbone and language-specific adapter modules. Each adapter is trained on language-specific data to address imbalances, while the shared backbone maintains efficiency. This segmentation allows the system to maintain low device complexity while improving word error rate through specialized language adapters.
Solution Approach 2:
The patent applies local quality by making different parts of the model have different properties - the acoustic model backbone remains shared and generic across languages, while language-specific adapter modules are customized for each language's characteristics. This allows the system to handle language-specific data imbalances without increasing overall device complexity.
2Reliability
If language-specific adapter layers are added to the end-to-end model, then word error rate decreases through better language-specific modeling, but device complexity increases
Solution Approach 1:
The patent merges language-specific adapter modules with the shared acoustic model backbone into a unified end-to-end training framework. The adapters are integrated seamlessly with the main model, allowing joint optimization during training. This merging reduces the practical complexity increase while maintaining the benefits of language-specific modeling for improved word error rate.
Solution Approach 2:
The acoustic model backbone serves as a universal component that handles common acoustic patterns across all languages, while language-specific adapters provide specialized functionality. This multi-functionality allows the system to achieve good performance across multiple languages without proportionally increasing device complexity.
3Loss of time
If the model processes speech in real-time with streaming architecture, then latency is reduced for interactive applications, but measurement precision of speech recognition decreases
Solution Approach 1:
The patent applies preliminary action by pre-training the acoustic model backbone on extensive multilingual data before deploying language-specific adapters. This pre-training ensures that the shared backbone already contains robust language-agnostic acoustic patterns, allowing the streaming architecture to achieve good accuracy even with limited context available during real-time processing.
Data Source
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AI summary
A method (400) of transcribing speech using a multilingual end-to-end (E2E) speech recognition model (115) includes receiving audio data (110) for an utterance (106) spoken in a particular native language, obtaining a language vector (115) identifying the particular language, and processing, using the multilingual E2E speech recognition model, the language vector and acoustic features (117) derived from the audio data to generate a transcription (1:20) for the utterance. The multilingual E2E speech recognition model includes a plurality of language-specific adaptor modules (300) that include one or more adaptor modules specific to the particular native language and one or more other adaptor modules specific to at least one other native language different than the particular native language. The method also includes providing the transcription for output.