Real-time Speech Translation System with Automated Source Selection
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Solution Overview
Problem
Current real-time speech translation systems suffer from low quality due to errors in automatic speech recognition (ASR) and machine translation (MT), which are exacerbated by noise and require human intervention to select the best source for translation.
Innovation Solution
A computer-implemented method that receives real-time input speech and human interpretation, transcribes and translates both using ASR and MT systems, and automatically selects the most credible source based on credibility scores to produce high-quality translations without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple parallel language sources are used to improve translation quality, then translation quality is improved, but device complexity and operational complexity increase due to requiring human operators to select the best source
Solution Approach 1:
The system automatically selects the best translation source using an automated selection module that evaluates credibility scores of multiple sources (direct ASR transcription, MT from source language, and MT from interpretation languages) without requiring human operator intervention. The module autonomously determines which source to use based on real-time quality assessment.
Solution Approach 2:
The system implements a feedback mechanism where the automated selection module continuously evaluates the quality and credibility of multiple translation sources in real-time, selecting the best source dynamically. This feedback loop ensures optimal translation quality while automating the source selection process that previously required human operators.
2Reliability
If multiple parallel language sources are used to improve translation quality, then translation quality is improved, but productivity decreases due to human operator involvement in source selection
Solution Approach 1:
The automated selection module autonomously and rapidly evaluates multiple translation sources and selects the best one in real-time without human intervention, maintaining high translation speed while ensuring quality through automated quality assessment and dynamic source selection.
3Speed
If ASR and MT systems are used for real-time speech translation, then real-time translation capability is achieved, but translation quality deteriorates due to errors in ASR and MT
Solution Approach 1:
The system merges multiple translation sources including direct ASR transcription of source speech, MT translation from source language, and MT translation from interpretation languages. By combining these diverse sources and automatically selecting the best one, the system achieves both real-time speed and improved quality through error balancing.
Solution Approach 2:
The system creates a composite translation approach by integrating multiple translation pathways (direct ASR, MT from source, MT from interpretation) and using an automated selection module to composite the best results, thereby achieving high-quality real-time translation that overcomes the limitations of individual ASR and MT systems.
4Reliability
If human operators are used to select the best translation source, then translation quality can be optimized, but ease of operation decreases and system complexity increases
Solution Approach 1:
The automated selection module autonomously evaluates the credibility and quality of multiple translation sources in real-time and automatically selects the best source without requiring human operator intervention, thereby maintaining translation quality while significantly improving ease of operation.
Solution Approach 2:
The system replaces the manual mechanical process of human operator selection with an automated computational selection module that uses algorithms to evaluate and select the best translation source, thereby eliminating the need for human operators while maintaining or improving translation quality.
Data Source
AI summary
A computer-implemented method of real time speech translation wherein at least a source speech and a human interpretation of the source speech are transcribed using an automatic speech recognition system and machine translated into a common language. A best source of data is then selected repeatedly and data from the best source are machine translated into at least one another language.