Google LLC (20240289563). SPEECH-TO-SPEECH TRANSLATION WITH MONOLINGUAL DATA simplified abstract

SPEECH-TO-SPEECH TRANSLATION WITH MONOLINGUAL DATA

Organization Name

Google LLC

Inventor(s)

Michelle Tadmor Ramanovich of Tel-Aviv (IL)

Eliya Nachmani of Tel-Aviv (IL)

Alon Levkovitch of Tel-Aviv (IL)

Byungha Chun of Tokyo (JP)

Yifan Ding of Tokyo (JP)

Nadav Bar of Raanana (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

Simplified Explanation: The patent application discusses training and utilizing a speech-to-speech translation system that can convert spoken utterances from a source language to a synthetic spoken utterance in a target language.

Key Features and Innovation:

• Speech-to-speech translation system for generating synthetic spoken utterances in a target language.
• Utilizes an unsupervised approach with monolingual speech data for training.
• Corresponds linguistically and para-linguistically to the source language utterance.

Potential Applications: This technology can be used in language translation services, communication devices, language learning tools, and international business interactions.

Problems Solved:

• Overcoming language barriers in real-time communication.
• Providing accurate and natural-sounding translations.
• Enhancing cross-cultural understanding and collaboration.

Benefits:

• Facilitates seamless communication between speakers of different languages.
• Improves accessibility to information and services for non-native speakers.
• Enhances user experience in multilingual environments.

Commercial Applications: The technology can be applied in translation apps, language interpretation services, global customer support centers, and international conferences.

Prior Art: Researchers can explore existing patents related to speech-to-speech translation systems, machine learning in language processing, and natural language generation.

Frequently Updated Research: Stay updated on advancements in machine learning algorithms for speech recognition, language translation models, and cross-lingual communication technologies.

Questions about Speech-to-Speech Translation Systems: 1. How does the unsupervised training approach differ from supervised methods in speech-to-speech translation systems? 2. What are the potential challenges in achieving accurate and contextually appropriate translations in real-time communication?

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.