Streaming Speech Translation with Chunked Prefix Commitment
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Solution Overview
Problem
Existing streaming end-to-end speech translation systems experience significant latency and quality issues due to frequent revisions of intermediate translation results, making it difficult to generate speech output in the target language in a timely and coherent manner.
Innovation Solution
The system processes speech data in chunks, performing beam search on each frame to identify candidate language tokens, committing the best candidate tokens as a prefix for the next chunk, and dynamically varying chunk sizes to account for word reordering across languages, thereby fixing the translation results within each chunk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If beam search is used to translate speech in real time, then translation speed is improved, but translation quality deteriorates due to frequent revisions
Solution Approach 1:
The patent segments the speech input into discrete time chunks and processes each chunk independently through beam search. This segmentation allows the system to commit to translation decisions for each chunk without continuous revision, resolving the contradiction between fast translation and quality stability.
Solution Approach 2:
The system performs beam search and selects candidate tokens in advance for each time chunk before moving to the next chunk. This preliminary action of committing to translations ensures quality stability while maintaining speed, as the system doesn't need to revise previous chunks once committed.
2Measurement precision
If intermediate translation results are continuously revised, then translation accuracy is improved, but latency increases
Solution Approach 1:
By dividing the translation process into discrete time chunks, the system achieves a balance where accuracy is improved through beam search within each chunk while latency is controlled by committing results at chunk boundaries rather than continuously revising.
Solution Approach 2:
The system uses feedback from the beam search process within each chunk to select candidate tokens, then commits these as prefixes for the next chunk. This structured feedback mechanism ensures accuracy without the continuous revision that causes latency.
3Stability of the object's composition
If translation results are committed as prefixes, then coherence is improved, but adaptability to language changes decreases
Solution Approach 1:
The patent segments the translation into time chunks where each chunk is processed independently. This allows the system to maintain coherence within each chunk while adapting to language changes between chunks, as each chunk can be optimized for its specific temporal context.
Solution Approach 2:
The system dynamically adjusts the translation commitment strategy by treating each time chunk as an independent unit. This dynamic approach allows the system to maintain coherence within chunks while remaining adaptable to language variations across different time periods.
Data Source
AI summary
A computer implemented method includes receiving speech data representative of speech in a first language The speech data is divided into chunks of speech data, each chunk comprising multiple temporally consecutive frames of acoustic information. Each temporally consecutive chunk of data is processed using beam search on each frame to identify candidate language tokens representing a second language different from the first language. A best candidate language token(s) is selected for each chunk as processed. The selected best candidate language token or tokens for each chunk of data is committed as a prefix for a next temporally consecutive chunk of data.