Google llc (20240289563). SPEECH-TO-SPEECH TRANSLATION WITH MONOLINGUAL DATA simplified abstract
Contents
SPEECH-TO-SPEECH TRANSLATION WITH MONOLINGUAL DATA
Organization Name
Inventor(s)
Michelle Tadmor Ramanovich of Tel-Aviv (IL)
Eliya Nachmani of Tel-Aviv (IL)
Alon Levkovitch of Tel-Aviv (IL)
Chulayuth Asawaroengchai of Zurich (CH)
SPEECH-TO-SPEECH TRANSLATION WITH MONOLINGUAL DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240289563 titled 'SPEECH-TO-SPEECH TRANSLATION WITH MONOLINGUAL DATA
The abstract of this patent application describes a speech-to-speech translation (S2ST) system that can process source audio data capturing spoken utterances in a source language and generate target audio data with synthetic spoken utterances in a target language that correspond to the source utterances.
- The S2ST system utilizes an unsupervised approach for training with monolingual speech data.
- The system aims to create synthetic spoken utterances in a target language that match the linguistic and para-linguistic aspects of the source utterances.
Potential Applications:
- Language translation services
- Communication assistance for multilingual individuals
- Language learning and practice tools
Problems Solved:
- Bridging language barriers in real-time communication
- Providing accurate and natural-sounding translations
- Enhancing accessibility for non-native speakers
Benefits:
- Improved cross-lingual communication
- Enhanced language learning experiences
- Increased accessibility for diverse language speakers
Commercial Applications:
- Language translation software for businesses
- Multilingual customer support services
- Educational language learning platforms
Questions about Speech-to-Speech Translation (S2ST): 1. How does the unsupervised training approach benefit the S2ST system? 2. What are the key challenges in developing accurate para-linguistic correspondences in the target language?
Frequently Updated Research: Ongoing research focuses on improving the accuracy and efficiency of S2ST systems through advanced machine learning algorithms and data processing techniques.
Original Abstract Submitted
training and/or utilizing a speech-to-speech translation (s2st) system that can be used to generate, based on processing source audio data that captures a spoken utterance in a source language, target audio data that includes a synthetic spoken utterance that is spoken in a target language and that corresponds, both linguistically and para-linguistically, to the spoken utterance in the source language. implementations that are directed to training the s2st system utilize an unsupervised approach, with monolingual speech data, in training the s2st system.
