Mass Luminosity, Inc. (20240220737). PROBABILISTIC MULTI-PARTY AUDIO TRANSLATION simplified abstract
PROBABILISTIC MULTI-PARTY AUDIO TRANSLATION
Organization Name
Inventor(s)
Teodor Atroshenko of Valencia (ES)
PROBABILISTIC MULTI-PARTY AUDIO TRANSLATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240220737 titled 'PROBABILISTIC MULTI-PARTY AUDIO TRANSLATION
- Simplified Explanation:**
This patent application describes a method for probabilistic multi-party audio translation, which involves processing input text of a communication session to generate translation data and enunciation data, and presenting the enunciation data based on a similarity score.
- Key Features and Innovation:**
- Utilizes a prediction model and a translation model to process input text.
- Generates translation data and enunciation data.
- Uses a sentence similarity model to calculate a similarity score.
- Presents enunciation data based on the similarity score.
- Potential Applications:**
- Multilingual communication platforms.
- Language learning tools.
- Conference call translation services.
- Problems Solved:**
- Facilitates real-time audio translation in multi-party communication.
- Improves accuracy and efficiency of audio translation services.
- Benefits:**
- Enhances communication between individuals speaking different languages.
- Streamlines the translation process in group conversations.
- Increases accessibility to multilingual content.
- Commercial Applications:**
- "Probabilistic Multi-Party Audio Translation Method for Enhanced Communication Services"
- Potential use in telecommunication companies, language learning platforms, and international business meetings.
- Questions about Probabilistic Multi-Party Audio Translation:**
1. How does the method determine the similarity score between translation data and enunciation data? 2. What are the potential challenges in implementing this technology in real-time communication settings?
- Frequently Updated Research:**
Stay updated on advancements in machine learning models for audio translation and improvements in natural language processing algorithms for multi-party communication.
Original Abstract Submitted
a method implements probabilistic multi-party audio translation. the method includes receiving input text of a communication session. the method further includes processing the input text with a prediction model and a translation model to generate translation data. the method further includes processing the translation data and enunciation data with a sentence similarity model to generate a similarity score. the method further includes presenting the enunciation data based on the similarity score.
