Speech Translation Model Dual-Task Training for Bias Reduction
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Solution Overview
Problem
Existing speech translation technologies face challenges with low translation quality and weak coherence of translation text, leading to low accuracy and inconvenience in user communication.
Innovation Solution
A training method for a speech translation model that includes executing a speech translation training task and an auxiliary training task simultaneously, with the auxiliary task aimed at reducing translation bias caused by speech recognition errors, to optimize robustness and generalization of speech translation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If speech translation model is trained using conventional methods, then training simplicity is maintained, but translation quality and coherence are poor
Solution Approach 1:
The training process is segmented into two distinct tasks: the speech translation training task for learning translation capabilities, and the auxiliary training task for reducing speech recognition bias. This segmentation allows each task to be optimized independently while working together to improve overall translation quality.
Solution Approach 2:
The auxiliary training task is performed in parallel during the training phase to preemptively reduce translation bias before it affects the final translation output. This preliminary action addresses the bias issue proactively rather than correcting it post-training.
2Ease of operation
If speech recognition is performed on speech input, then speech to text conversion is achieved, but speech recognition bias is introduced
Solution Approach 1:
The auxiliary training task converts the harmful speech recognition bias into a beneficial training signal by using biased speech recognition texts as training data. The model learns to recognize and compensate for the bias patterns, transforming the previously harmful effect into a useful preprocessing step that actually helps the translation task.
Solution Approach 2:
The auxiliary training task acts as an intermediary between speech recognition and translation by processing the biased speech recognition output and preparing it for translation. This intermediate processing step reduces the bias before the text enters the translation model.
3Adaptability or versatility
If domain data such as ASR texts are introduced to enhance translation, then translation coverage is improved, but translation coherence deteriorates
Solution Approach 1:
The model's parameters are adjusted through dual-task training to change its behavior when processing domain-specific texts. The auxiliary training task modifies the model's internal representations to maintain coherence even when handling specialized terminology and structures from domain data.
Data Source
AI summary
A training method for a speech translation model, a speech translation method, an apparatus, and a device are provided. The training method includes: after entering a model training phase, controlling the speech translation model to execute a speech translation training task; controlling the speech translation model to simultaneously execute an auxiliary training task of the speech translation training task; and adjusting network parameters of the speech translation model according to the speech translation training task and the auxiliary training task, so as to obtain an updated speech translation model after training.