Token-Based Speech Translation via Intermediate Language
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Solution Overview
Problem
Existing translation and transcription systems face challenges in efficiency, latency, and computational resource requirements, particularly in handling multiple language pairs and real-time applications, due to the complexity of language nuances and the need for extensive bilingual data and computational resources.
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 efficiency, with smaller encoders for transcription and larger encoders for translation to capture context and nuance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional translation systems use extensive bilingual data and large computational models for each language pair, then translation accuracy is improved, but computational resource requirements and system complexity increase significantly
Solution Approach 1:
The patent introduces an intermediary language (English) as a bridge between source and target languages. Instead of requiring direct bilingual training data for every language pair, the system translates from the source language to the intermediary language, then from the intermediary language to the target language. This mediator approach reduces the need for extensive direct bilingual corpora and simplifies the overall system architecture while maintaining translation quality.
Solution Approach 2:
The patent creates a universal translation framework where a single intermediary language model serves multiple language pairs. By training one model on the intermediary language, the system can handle translations between any source and target language combination that passes through this intermediary, eliminating the need to develop and maintain separate specialized models for each language pair.
2Measurement precision
If large computational models are used for translation tasks, then translation quality is improved, but real-time processing capability and efficiency decrease
Solution Approach 1:
The patent divides the translation task into two separate stages: first translating from the source language to the intermediary language, then translating from the intermediary language to the target language. This segmentation allows each stage to use appropriately-sized models optimized for its specific task, improving overall efficiency while maintaining quality. The intermediate step breaks down the complex direct translation into manageable segments that process faster.
3Measurement precision
If multiple specialized translation models are trained for different language pairs, then translation accuracy for specific pairs is improved, but the number of models and computational resources required increases
Solution Approach 1:
The patent establishes a universal intermediary language model that can be applied to any language pair requiring translation. Instead of training separate specialized models for each language combination, this single multi-functional model handles translations from any source language to any target language through the intermediary, dramatically reducing the total number of models needed while maintaining adaptability across different language pairs.
Data Source
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.