End-to-End Speech Translation Neural Network
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Solution Overview
Problem
Conventional speech-to-speech translation systems rely on cascaded approaches that suffer from error compounding, increased computational requirements, higher inference latency, and inability to retain paralinguistic and non-linguistic information such as voice, intonation, and emotion during translation.
Innovation Solution
A machine-learned translation model that performs direct and text-free speech-to-speech translation using an attention-based sequence-to-sequence neural network, trained end-to-end to map acoustic feature representations of speech in one language directly into acoustic feature representations of speech in another language, with the ability to synthesize translated speech using the voice of a target speaker and incorporate auxiliary speech recognition models for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cascaded systems (ASR + MT + TTS) are used for speech-to-speech translation, then translation functionality is achieved, but computational requirements increase and inference latency increases
Solution Approach 1:
The patent combines three separate cascaded systems (automatic speech recognition, text-to-text machine translation, and text-to-speech synthesis) into a single unified end-to-end neural network model. This merging eliminates the need for multiple distinct computational steps, directly reducing system complexity while maintaining translation functionality.
Solution Approach 2:
The unified model is architecturally segmented into distinct functional components (encoder, attention mechanism, decoder) that work together in sequence, allowing each component to specialize in specific aspects of translation while operating as an integrated whole, thus improving computational efficiency.
2Productivity
If cascaded systems are used for speech-to-speech translation, then translation functionality is achieved, but inference latency increases
Solution Approach 1:
By merging ASR, MT, and TTS into a single end-to-end model, the system eliminates the sequential processing delays inherent in cascaded systems. The model processes translation in one continuous operation rather than through multiple discrete steps, directly reducing inference latency.
Solution Approach 2:
The end-to-end model maintains continuous processing throughout the translation pipeline, avoiding the start-stop nature of cascaded systems where each component must complete before the next begins. This continuity eliminates idle time and reduces overall inference latency.
3Reliability
If cascaded systems are used for speech-to-speech translation, then translation functionality is achieved, but error compounding occurs
Solution Approach 1:
The unified end-to-end model processes translation in a single integrated operation, eliminating the multiple error-prone interfaces between separate systems. By removing the boundaries between ASR, MT, and TTS components, the system prevents error propagation and compounding that occurs in cascaded architectures.
Solution Approach 2:
The model incorporates attention mechanisms that provide feedback loops between processing stages, allowing the system to continuously refine its predictions and correct errors in real-time during the translation process, thereby improving overall accuracy.
4Loss of information
If cascaded systems are used for speech-to-speech translation, then translation functionality is achieved, but paralinguistic information is lost
Solution Approach 1:
The unified model merges all processing functions into a single system that handles both linguistic translation and paralinguistic feature preservation simultaneously. This integration allows the model to maintain speaker characteristics, intonation, and emotion throughout the translation process without requiring separate processing pipelines.
Solution Approach 2:
The end-to-end model is designed with universal capabilities to handle multiple functions concurrently: linguistic translation, speaker voice preservation, intonation maintenance, and emotional tone retention. This multi-functionality allows a single system to preserve all information types without increasing overall system complexity.
Data Source
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AI summary
The present disclosure provides systems and methods that train and use machine-learned models such as, for example, sequence-to-sequence models, to perform direct and text-free speech-to-speech translation. In particular, aspects of the present disclosure provide an attention-based sequence-to-sequence neural network which can directly translate speech from one language into speech in another language, without relying on an intermediate text representation.