Multilingual ASR Model with Joint Language Identification
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Solution Overview
Problem
Current automatic speech recognition (ASR) models are primarily monolingual, failing to effectively handle bilingual speakers and increasing computational and storage costs when separate multilingual and language identification models are used for code-switching and speech translation.
Innovation Solution
A multilingual ASR model is developed that integrates a first encoder for generating higher-order feature representations, a second encoder for further processing, and a language identification predictor to generate language prediction representations, allowing for simultaneous transcription and language prediction without relying on oracle language identification, operating in both streaming and non-streaming modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate multilingual and language identification models are used, then language identification accuracy is improved, but computational and storage costs increase
Solution Approach 1:
The patent combines language identification and speech recognition into a single integrated model architecture. The language ID predictor and speech recognition decoder share the same encoder outputs and are trained jointly, eliminating the need for separate models while maintaining both language identification accuracy and speech recognition performance. This merging reduces computational overhead and storage requirements compared to using separate models.
Solution Approach 2:
The integrated model serves multiple functions simultaneously: it performs both language identification and speech recognition tasks. The same neural network components (encoders, predictors, and decoders) are used to extract features for both language classification and speech transcription, making the system multi-functional without requiring duplicate model instances.
2Measurement precision
If oracle language identification is used, then speech recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The model performs language identification internally as part of its own speech recognition process without requiring external oracle input. The language ID predictor generates language predictions from the same acoustic features used for speech recognition, allowing the system to serve its own language identification needs autonomously and integrate this information into the recognition process.
Solution Approach 2:
The patent merges language identification and speech recognition into a single joint training objective. Both tasks share the same encoder and are optimized together through a combined loss function, eliminating the need for separate oracle language identification systems and reducing overall system complexity while maintaining accuracy.
3Loss of time
If streaming mode is implemented, then latency is reduced, but accuracy may deteriorate
Solution Approach 1:
The model dynamically adapts its processing based on the streaming context. The causal encoder processes acoustic frames in real-time as they arrive, while the language ID predictor and decoder continuously update predictions with each new frame. This dynamic streaming approach allows the system to provide low-latency results while progressively improving accuracy as more context becomes available.
Solution Approach 2:
The integrated model uses feedback from the language ID predictor to guide the speech recognition decoder in streaming mode. The language predictions provide contextual information that helps the decoder make more accurate predictions with limited input frames, mitigating the accuracy-latency trade-off through iterative refinement as more acoustic frames are processed.
Data Source
AI summary
A method includes receiving a sequence of acoustic frames as input to an automatic speech recognition (ASR) model. The method also includes generating, by a first encoder, a first higher order feature representation for a corresponding acoustic frame. The method also includes generating, by a second encoder, a second higher order feature representation for a corresponding first higher order feature representation. The method also includes generating, by a language identification (ID) predictor, a language prediction representation based on a concatenation of the first higher order feature representation and the second higher order feature representation. The method also includes generating, by a first decoder, a first probability distribution over possible speech recognition hypotheses based on a concatenation of the second higher order feature representation and the language prediction representation.