Speech Translation Pipeline Using Intermediate Text and Split Look-Ahead
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Solution Overview
Problem
Existing translation and transcription systems face challenges in efficiency, latency, and computational resource requirements due to the need for numerous direct translation models between language pairs and the complexity of handling nuanced language 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 and enhance processing efficiency, leveraging neural networks like RNN-T and Transformer models to handle sequential data and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct translation models are used for each language pair, then translation accuracy is improved, but the number of models and computational resources required increases significantly
Solution Approach 1:
The patent introduces an intermediate language (such as English) as a mediator in the translation process. Instead of directly translating from source language to target language using a dedicated model, the system translates through the intermediate language using two separate models: a first model for source-to-intermediate translation and a second model for intermediate-to-target translation. This intermediary approach reduces the number of direct translation models needed while maintaining translation accuracy across multiple language pairs.
2Measurement precision
If larger look-ahead encoders are used for translation, then contextual understanding and translation quality are improved, but processing time and latency increase
Solution Approach 1:
The patent applies different look-ahead encoder sizes to different processing tasks based on their specific requirements. The first look-ahead encoder uses a smaller size optimized for transcription tasks where speed is critical, while the second look-ahead encoder uses a larger size optimized for translation tasks where contextual understanding is more important. This localized optimization allows each component to be tuned for its specific function, balancing quality and latency requirements.
3Measurement precision
If more comprehensive language context is processed, then transcription and translation accuracy are improved, but computational resource consumption increases
Solution Approach 1:
The patent segments the language processing task into distinct functional components: a first neural network model for transcription (source language speech to intermediate language text) and a second neural network model for translation (intermediate language text to target language text). Each model processes specific segments of the data with appropriate computational resources, avoiding the need to process entire contexts through a single oversized model. This segmentation allows optimized resource allocation for each specific task.
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 transcription of the source language speech into text of the source language using a first look-ahead encoder to define source language text; and performing a first token-based translation of the source language speech into text of a target language using a second look-ahead encoder to define target language text, wherein the first look-ahead encoder is smaller than the second look-ahead encoder.