Speech Translation Model Training via Text Pre-training
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Solution Overview
Problem
Existing methods for end-to-end speech translation face challenges due to the scarcity of training data and the modal gap between speech and text, leading to inadequate training effects and accuracy in translating speech signals from a source language to a target text.
Innovation Solution
A method involving the construction of a candidate speech translation model based on a trained text translation model and a speech recognition model, using a combination of source language speech and text samples for training, to improve the translation accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If indirect translation through speech recognition or machine translation model training is used, then the translation can be realized, but the accuracy is inadequate due to the modal gap between speech and text
Solution Approach 1:
The patent segments the speech translation task into two separate model training stages: first training a text translation model on text-to-text pairs, then training a speech translation model on speech-to-text pairs. This segmentation allows each model to be optimized for its specific input type while maintaining translation quality, resolving the contradiction between translation accuracy and model construction complexity.
Solution Approach 2:
The patent applies preliminary action by first training the text translation model using abundant text-to-text parallel data before training the speech translation model. This preliminary training establishes a solid foundation for translation capabilities that can then be adapted to speech inputs, improving overall translation accuracy while managing complexity through staged development.
2Productivity
If end-to-end speech translation is implemented, then direct translation from speech signal to target text is achieved, but the training effect is inadequate due to scarcity of training data
Solution Approach 1:
The patent performs preliminary training of the text translation model using abundant text-to-text parallel data before training the speech translation model with limited speech-to-text data. This preliminary action maximizes the utilization of available training resources and establishes strong translation capabilities that transfer to the speech translation task, improving training effectiveness despite data scarcity.
Solution Approach 2:
The patent changes the input modality parameter from speech to text in the first training stage, allowing the model to learn translation patterns from text inputs. This parameter change enables effective use of text data to initialize translation capabilities, which are then fine-tuned for speech inputs in the second stage, overcoming the limitation of scarce speech training data.
Data Source
AI summary
A method for training a speech translation model includes: obtaining a trained first text translation model and a speech recognition model, and constructing a candidate speech translation model to be trained based on the first text translation model and the speech recognition model; obtaining at least one of a first sample source language speech or a first sample source language text to obtain a training sample of the candidate speech translation model; and training the candidate speech translation model based on the training sample until the training is completed, and obtaining a trained target speech translation model.