Voice Translation Semantic Integrity Segmentation
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Solution Overview
Problem
Current voice translation technologies face challenges in accuracy and speed due to issues with voice segment segmentation, semantic information loss, and high time complexity, particularly when dealing with varying voice speeds and incomplete semantic segments.
Innovation Solution
The proposed method involves segmenting voice signals into segments using voice activity detection, determining semantic integrity between segments, and reorganizing segments as needed to ensure complete semantic information, using a combination of convolutional neural networks and attention mechanisms for encoding and decoding, and employing multi-kernel window convolution networks to handle varying voice speeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If voice signals are segmented using voice activity detection, then processing speed is improved, but semantic information may be lost due to incomplete segments
Solution Approach 1:
The voice signal is divided into multiple voice segments using voice activity detection, allowing parallel processing of segments to improve speed while maintaining semantic integrity through integrity determination mechanisms
Solution Approach 2:
The system determines semantic integrity of segmented voice signals by comparing voice features across segments, providing feedback to identify and correct incomplete segments, thus preventing semantic information loss while maintaining processing efficiency
2Measurement precision
If traditional voice translation methods are used, then implementation is simpler, but translation accuracy is lower
Solution Approach 1:
The system performs preliminary voice segmentation and semantic integrity determination before translation, preparing optimized input segments that improve translation accuracy while managing complexity through structured preprocessing
Solution Approach 2:
The translation system combines multiple components including voice activity detection, semantic integrity determination, and translation engines into a composite system that achieves high accuracy through coordinated operation of specialized subsystems
3Productivity
If voice segments are processed independently, then processing efficiency is improved, but semantic coherence between segments deteriorates
Solution Approach 1:
Semantic integrity determination acts as feedback between independently processed segments, comparing voice features to ensure semantic coherence is maintained across segment boundaries while preserving processing efficiency
Solution Approach 2:
The system merges segmentation benefits with integrity verification, combining independent segment processing with cross-segment semantic analysis to maintain both efficiency and coherence
Data Source
AI summary
A method for voice translation includes: receiving a voice signal of a first language; obtaining a plurality of voice segments forming the voice signal; determining integrity of a first voice segment with respect to a second voice segment based on a voice feature of the first voice segment and a voice feature of the second voice segment; obtaining an output voice segment based on the integrity of the first voice segment with respect to the second voice segment; and outputting a text in a second language corresponding to the voice signal of the first language based on the output voice segment.