Iterative Speech Translation System Training
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Solution Overview
Problem
Current speech translation systems require extensive data resources and labor-intensive processes for training, including independent annotation and optimization of speech recognition and translation engines, which is inefficient and costly, especially for languages with limited written corpora.
Innovation Solution
An iterative language translation system that combines automatic speech recognition and machine translation components to adapt and train together using human simultaneous translator data, allowing for unsupervised training and correction of errors directly in the field, reducing the need for large datasets and independent training steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If independent annotation and training of speech recognition and translation engines are performed separately, then each component can be optimized individually, but the development time and cost increase substantially
Solution Approach 1:
The patent combines speech recognition and machine translation training into a unified iterative process. The system processes bilingual speech data through both ASR and MT components simultaneously, allowing them to learn from each other's outputs and adapt together, thereby reducing the time required for separate independent training while maintaining component optimization.
Solution Approach 2:
The patent implements feedback loops where the ASR component's hypotheses are fed to the MT component, and the translated hypotheses are fed back to retrain the ASR component. This iterative feedback mechanism allows both components to continuously improve together using the same bilingual speech data, eliminating the need for separate training phases and reducing overall development time.
2Reliability
If extensive data resources are collected and annotated for training, then system performance improves, but the labor intensity and cost increase
Solution Approach 1:
The patent makes the bilingual speech corpus serve multiple functions simultaneously: it trains the ASR component, trains the MT component, and provides feedback for iterative improvement of both. This multi-functional use of the same data resource eliminates the need for separate training datasets for each component, reducing annotation labor while maintaining high system performance.
Solution Approach 2:
The system uses its own generated hypotheses and translations as training data for iterative improvement. The ASR hypotheses and MT translations are automatically fed back into the training process, allowing the system to self-improve without requiring additional manually annotated data, thereby reducing ongoing labor requirements while maintaining performance gains.
3Reliability
If speech and translation engines are trained independently first, then each engine can be optimized, but the system cannot adapt to field errors or learn from simultaneous translator data
Solution Approach 1:
The patent implements continuous feedback loops where system outputs are fed back into the training process. The ASR hypotheses and MT translations are used to retrain both components iteratively, enabling the system to adapt to field errors and learn from actual usage data. This feedback mechanism provides both optimization and adaptability simultaneously.
Solution Approach 2:
The patent transforms the static independent training process into a dynamic iterative process. The system continuously adapts its models based on feedback from its own operations, allowing it to evolve and improve over time in the field. This dynamic approach enables both engine optimization and field adaptability by making the training process ongoing rather than one-time.
Data Source
AI summary
An iterative language translation system includes multiple communicatively connected statistical speech translation systems. The system includes an automatic speech recognition component adapted to recognize spoken language in a source language and to create a source language hypothesis. A machine translation component is adapted to translate the source language hypothesis into a target language. The system also includes a second automatic speech recognition component and second machine translation component. The translation results are used to adapt the automatic speech recognition components and the language hypotheses are used to adapt the machine translation components.