Simultaneous Spoken-Language Machine Translation Re-Translation
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Solution Overview
Problem
Current simultaneous speech recognition and machine translation systems face challenges in achieving high accuracy and low latency while maintaining stability, often resulting in 'flickering' translations due to the need for continuous re-translation of spoken content as more audio context becomes available.
Innovation Solution
The system employs a re-translation approach with techniques like biased beam search and post-processing of ASR outputs to stabilize translations, ensuring that only stable tokens are provided to the MT module, and uses incremental processing to generate translations without waiting for complete sentences, allowing for immediate updates and corrections based on additional speech context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If re-translation is performed continuously as more speech context is received, then translation accuracy is improved, but translation stability deteriorates causing flickering output
Solution Approach 1:
The system dynamically adjusts the translation process by performing re-translation at different stages: initial translation provides immediate output, followed by conditional re-translation when new speech context arrives. The translation model adapts its behavior based on the availability of additional context, balancing between updating for accuracy and maintaining stability by only re-translating when necessary.
Solution Approach 2:
The system implements feedback mechanisms where translation outputs are monitored and compared across multiple passes. When re-translation occurs, the system evaluates whether the new translation significantly differs from the previous one, and only updates the output if the change meets a threshold, thereby reducing unnecessary flickering while maintaining accuracy improvements.
2Loss of time
If translation is performed incrementally without waiting for complete sentences, then latency is reduced, but accuracy deteriorates due to limited context
Solution Approach 1:
The system performs preliminary translation on partial speech segments as soon as they are available, providing early output with low latency. This initial translation is then refined through subsequent re-translation passes when additional speech context becomes available, allowing the system to deliver quick initial results that are subsequently improved for higher accuracy.
Solution Approach 2:
The translation process operates in periodic cycles: initial translation of available speech, followed by re-translation cycles when new speech context arrives. This periodic re-translation allows the system to balance between providing timely incremental output and improving accuracy through repeated translation with expanding context windows.
3Measurement precision
If multiple translation passes are performed on the same speech segment, then accuracy is improved, but processing time increases
Solution Approach 1:
The system applies re-translation selectively to specific speech segments rather than uniformly processing all text. By identifying which segments benefit most from re-translation based on context changes and translation stability metrics, the system concentrates computational resources where they provide the most accuracy improvement, reducing overall processing time compared to uniform multi-pass translation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for re-translation for simultaneous, spoken-language machine translation. In some implementations, a stream of audio data comprising speech in a first language is received. A transcription for the speech in the stream of audio data is generated using an automated speech recognizer through a series of updates. A translation of the transcription into a second language is generated using a machine translation module. The translation is generated with translation iterations that translate increasing amounts of the transcription, including re-translating previously portions of the transcription. A series of translation updates are provided to a client device based on the translation iterations.