Speech Translation Pipeline Using an Intermediate Language
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Solution Overview
Problem
Existing translation and transcription systems face challenges in efficiency, latency, and computational resource management due to the need for numerous direct translation models between language pairs and the complexity of handling nuanced linguistic contexts.
Innovation Solution
A token-based translation and transcription system using an intermediate language and differently-sized look-ahead encoders to reduce the number of translation models required and enhance processing efficiency, with smaller encoders for transcription and larger encoders for translation to capture broader contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If direct translation models are used for each language pair, then translation accuracy may be maintained, but the number of required models and computational resources increase significantly
Solution Approach 1:
The patent introduces an intermediate language (e.g., English) as a mediator between source and target languages. Instead of creating direct translation models for every language pair, the system translates from the source language to the intermediate language, then from the intermediate language to the target language. This reduces the number of required models from O(n²) to O(2n), where n is the number of languages, while maintaining translation quality through the use of a standardized intermediate representation.
Solution Approach 2:
The intermediate language serves as a universal intermediary that can handle translations between any language pair. A single intermediate language model can be used for all source languages, and a single target language model can handle all target languages, making the system multi-functional and scalable without requiring language-specific model pairs.
2Reliability
If larger encoders are used to capture broader contextual information, then translation accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies different encoder sizes to different functional requirements within the system. Smaller, more efficient encoders are used for transcription tasks where speed is critical, while larger encoders with broader contextual windows are used specifically for translation tasks where accuracy and contextual understanding are paramount. This local differentiation optimizes resource allocation based on task-specific needs.
Solution Approach 2:
The system segments the translation process into distinct stages (transcription to intermediate language, then intermediate language to target language) with different encoder configurations for each stage. This allows the system to use computationally intensive large encoders only where necessary (in the translation stage) while using lighter encoders for the transcription stage, reducing overall computational burden.
3Adaptability or versatility
If more translation models are deployed to handle diverse language pairs, then language coverage improves, but system scalability and maintenance become more difficult
Solution Approach 1:
The intermediate language acts as a universal interface that enables the system to handle any language pair through a standardized two-stage process. New languages can be added by training only two new models (source to intermediate, and intermediate to target) rather than requiring models for every possible language pair, dramatically improving scalability and reducing maintenance complexity.
Solution Approach 2:
The standardized intermediate language serves as a mediator that decouples source and target language models, allowing independent development and deployment. This intermediary layer simplifies system architecture and makes the system highly adaptable to new language pairs while maintaining consistent translation quality through the standardized intermediate representation.
Data Source
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AI summary
A computer-implemented method, computer program product and computing system for: receiving speech in a source language to define source language speech; performing a first token-based translation of the source language speech into text of an intermediate language to define intermediate language text; and performing a second token-based translation from the intermediate language text into text of a target language to define target language text.