Audio-to-Audio Translation With Preserved Speaker Characteristics
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Solution Overview
Problem
Existing audio translation methods often lose context and emotion in translations between different languages, introducing errors and latency due to text-based conversions, making real-time conversations challenging.
Innovation Solution
An end-to-end machine-learning system that converts audio streams directly into phonemes of a target language, predicts phoneme durations, and uses a synthesizer to generate output speech with preserved speaker characteristics, reducing latency and errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If text-based translation methods are used, then translation functionality is provided, but context and emotion are lost in translation
Solution Approach 1:
The patent replaces the mechanical text-based translation system with an audio-to-audio neural translation system. The encoder directly processes audio streams and outputs translated audio, eliminating the need for text conversion steps. This substitution preserves context and emotion by working directly with audio signals rather than converting to text, thereby resolving the information loss problem while maintaining system functionality.
Solution Approach 2:
The patent introduces an audio encoder as an intermediary component that bridges the source audio and target audio. The encoder processes the input audio stream, extracts linguistic and paralinguistic features, and generates the translated audio output. This intermediary preserves contextual information and emotional cues that would be lost in text-based systems, while the modular architecture keeps complexity manageable.
2Productivity
If audio is converted to text, translated, and converted back to audio, then translation is achieved, but errors and latency are introduced
Solution Approach 1:
The patent merges the audio encoding, translation, and audio synthesis functions into a single integrated audio-to-audio model. By combining these previously separate steps (audio-to-text conversion, text translation, text-to-audio conversion) into one unified neural network, the system eliminates multiple conversion points where errors could occur and latency could accumulate, thereby simultaneously improving both speed and accuracy.
Solution Approach 2:
The patent implements continuous audio stream processing where the encoder processes audio in real-time without discrete conversion stops. The model maintains continuous processing of the audio stream, preserving the temporal continuity and reducing latency compared to batch processing through multiple conversion stages. This continuous action ensures both speed and fidelity of the translation.
3Loss of time
If traditional translation systems are used, then language translation is provided, but real-time conversation is difficult due to latency
Solution Approach 1:
The patent performs preliminary processing of audio features and linguistic patterns during the training phase, enabling the model to make rapid predictions during real-time translation. The encoder is pre-trained on large datasets to recognize and translate speech patterns efficiently, reducing the computational burden during actual conversation and minimizing latency.
Solution Approach 2:
The patent implements a dynamic translation system that adapts processing speed and resource allocation based on the input audio characteristics and conversation context. The model can adjust its processing depth and output rate dynamically, optimizing for low latency in conversational scenarios while maintaining high accuracy when needed, thereby improving both response time and conversation ease.
Data Source
AI summary
A method includes receiving an audio stream from a first user associated with a first client device, wherein the audio stream is spoken in a first language by the first user. The method further includes retrieving translation data associated with a second user. The method further includes converting a first portion of the audio stream received from the first user into a plurality of phonemes of a second language, wherein the second language is defined by the language preference. The method further includes predicting a respective duration of each of the phonemes in the plurality of phonemes. The method further includes outputting, by a synthesizer, a first portion of output speech that includes the plurality of phonemes where each of the phonemes in the plurality of phonemes has the respective duration. The method further includes providing the first portion of output speech to the second user device.