Real-Time Speech Translation Cascade Architecture
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Solution Overview
Problem
Conventional machine translation methods require high processing power and fail to provide real-time, accurately translated results, limiting the effectiveness of simultaneous interpretation in multilingual communication settings such as international conferences and call centers.
Innovation Solution
A cascade of machine learning models is used to transcribe, segment, compress, and translate spoken speech in real-time, dynamically adjusting segmentation and compression to minimize latency, enabling near-simultaneous interpretation by leveraging automatic speech recognition, machine translation, and text-to-speech technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine translation methods are used, then translation accuracy can be maintained, but processing speed and real-time performance deteriorate due to high processing power requirements
Solution Approach 1:
The patent segments the translation process into multiple specialized models arranged in a cascade architecture: speech recognition model, translation model, and speech synthesis model. Each model handles a specific subtask, allowing parallel processing and optimization of individual components for both accuracy and speed.
Solution Approach 2:
The system dynamically adjusts processing parameters including segmentation granularity, compression levels, and model selection based on real-time latency requirements and computational resources available, enabling adaptive optimization of the accuracy-speed tradeoff.
2Reliability
If conventional machine translation methods are used, then comprehensive translation can be achieved, but latency increases making real-time interpretation impossible
Solution Approach 1:
The system performs preliminary actions by pre-processing speech into segments and preparing translation candidates before complete speech is received. The cascade models begin processing as soon as sufficient audio data is captured, reducing overall latency while maintaining translation quality.
Solution Approach 2:
The patent implements skipping mechanisms where less critical processing steps are accelerated or bypassed when latency thresholds are exceeded, allowing the system to deliver translations closer to real-time while maintaining acceptable accuracy through selective optimization of processing stages.
Data Source
AI summary
A method for providing automatic interpretation may include receiving, by a processor, audible speech from a speech source, generating, by the processor, in real-time, a speech transcript by applying an automatic speech recognition model on the speech, segmenting, by the processor, the speech transcript into speech segments based on a content of the speech by applying a segmenter model on the speech transcript, compressing, by the processor, the speech segments based on the content of the speech by applying a compressor model on the speech segments, generating, by the processor, a translation of the speech by applying a machine translation model on the compressed speech segments, and generating, by the processor, audible translated speech based on the translation of the speech by applying a text to speech model on the translation of the speech.