Back-Translation Training for Unwritten Speech Translation Models
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Solution Overview
Problem
Conventional speech translation systems face challenges in effectively training models for unwritten languages due to limited and scarce training data, leading to inaccurate translations, especially when direct speech translation is required.
Innovation Solution
The use of a back-translation technique with a back-translation model paired with a speech translation model to expand training data, combined with discretization of continuous speech signals into discrete units, enhances the training process and improves model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech translation systems are used for unwritten languages, then the system structure remains simple, but the translation accuracy deteriorates due to limited training data
Solution Approach 1:
The back-translation model generates supplementary training data in advance by translating target language text back to source language speech, creating additional training samples before the main translation model training process. This preliminary data generation resolves the contradiction by providing more training data without requiring additional real-world speech recordings.
Solution Approach 2:
The back-translation model acts as an intermediary that bridges the gap between available target language text data and the needed source language speech training data. It converts text data into synthetic speech data that can be used to train the speech translation model, thus resolving the data scarcity issue for unwritten languages.
2Measurement precision
If more training data is collected for unwritten languages, then the translation accuracy improves, but the data collection complexity and time increase
Solution Approach 1:
Instead of collecting original speech data from unwritten languages, the system creates copies/synthetic versions by back-translating from target language text. This copying approach generates training data instantly without time-consuming field recording, thus improving translation accuracy while avoiding time loss in data collection.
Solution Approach 2:
The system uses its own target language translation capabilities to generate source language training data through back-translation. This self-service mechanism eliminates the need for external data collection efforts, resolving the contradiction by providing unlimited training data generation without time investment.
3Measurement precision
If a back-translation model is introduced to expand training data, then the translation model accuracy improves, but the system complexity increases
Solution Approach 1:
The back-translation model serves multiple functions: it generates training data for the speech translation model, provides data augmentation, and enables the system to handle unwritten languages. This multi-functionality justifies the added complexity by delivering comprehensive benefits across different aspects of the translation system.
Solution Approach 2:
Instead of translating source speech to target text directly, the system introduces a reverse process where target text is translated back to source speech. This inverted approach creates a complementary training pathway that improves the main translation model's accuracy, making the additional complexity worthwhile.
4Adaptability or versatility
If continuous speech signals are discretized into discrete units, then the training sample diversity improves, but the processing complexity increases
Solution Approach 1:
The continuous speech signal is segmented into discrete units or tokens, transforming it into a form that can be more easily processed and compared. This segmentation increases training sample diversity by creating distinct categorical representations, while the standardized segmentation process keeps the added complexity manageable.
Data Source
AI summary
The present disclosure relates to a training method for a translation model, a translation method and device. Provided is a training method for a translation model, and the translation model is capable of converting data of a first type into data of a second type. The training method for a translation model comprises: applying sample data of a second type to a backtranslation model associated with the translation model to obtain training sample data, and training the translation model on the basis of the training sample data.