End-to-End Speech Recognition and Translation System
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Solution Overview
Problem
Existing speech recognition and translation systems suffer from error accumulation, high computational cost, and poor real-time performance due to serial processing schemes, where speech recognition errors are transmitted to translation systems, leading to final result errors and increased computational burden.
Innovation Solution
An end-to-end system incorporating an acoustic encoder and a multi-task decoder with self-attention mechanisms, along with a semantic invariance constraint module, processes speech recognition and translation tasks simultaneously, avoiding error accumulation and optimizing computational efficiency through down-sampling and re-encoding of acoustic features and using task labels for multi-task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If serial processing is used for speech recognition and translation, then the system can process tasks in sequence, but error accumulation occurs and real-time performance deteriorates
Solution Approach 1:
The patent merges speech recognition and speech translation into a single unified neural network model. The model shares acoustic encoding parameters between both tasks and processes them simultaneously through a joint training framework, eliminating the sequential error propagation inherent in separate systems while maintaining real-time performance through integrated processing.
Solution Approach 2:
The patent creates a universal model that performs multiple functions - both speech recognition and speech translation - within a single system. The model uses task labels to differentiate between recognition and translation tasks while sharing underlying acoustic features and encoding mechanisms, allowing it to adapt to different objectives without requiring separate specialized systems.
2Reliability
If serial processing with separate systems is used, then each task can be optimized independently, but computational cost increases
Solution Approach 1:
The patent combines two separate computational systems into one unified model, eliminating redundant acoustic encoding operations. By sharing the acoustic encoder and feature extraction pipelines between recognition and translation tasks, the system reduces overall computational load while maintaining independent optimization capabilities through task-specific decoding and loss functions.
Solution Approach 2:
The universal model achieves multiple task objectives through a single computational framework, reducing the need for separate system infrastructures. The model maintains task-specific optimization through differentiated training objectives and task labels while leveraging shared representations to minimize redundant computations across recognition and translation operations.
3Ease of manufacture
If separate speech recognition and translation systems are used, then each system can be independently trained, but the system complexity increases
Solution Approach 1:
The patent merges the training processes of recognition and translation systems into a unified multi-task training framework. The model uses task labels to differentiate training objectives while sharing acoustic encoding parameters, allowing simultaneous optimization of both tasks through a single training pipeline rather than requiring separate independent training processes.
Solution Approach 2:
The universal training framework handles multiple tasks through a single model architecture with task-specific adaptations. The system maintains ease of training by using differentiated loss functions and task labels while reducing overall system complexity by eliminating the need to manage, deploy, and maintain separate recognition and translation systems independently.
Data Source
AI summary
Disclosed are an end-to-end system for speech recognition and speech translation and an electronic device. The system comprises an acoustic encoder and a multi-task decoder and a semantic invariance constraint module, and completes two tasks for speech recognition and speech translation. In addition, according to the characteristic of the semantic consistency of texts between different tasks, semantic constraints are imposed on the model to learn high-level semantic information, and the semantic information can effectively improve the performance of speech recognition and speech translation. The application has the following advantages that the error accumulation problem of serial system is avoided, and the calculation cost of the model is low and the real-time performance is very high.