Roblox Corporation (20240304210). ARTIFICIAL LATENCY FOR MODERATING VOICE COMMUNICATION simplified abstract
Contents
ARTIFICIAL LATENCY FOR MODERATING VOICE COMMUNICATION
Organization Name
Inventor(s)
Mahesh Kumar Nandwana of Sunnyvale CA (US)
Philippe Clavel of Belmont CA (US)
Morgan Mcguire of Vancouver (CA)
ARTIFICIAL LATENCY FOR MODERATING VOICE COMMUNICATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240304210 titled 'ARTIFICIAL LATENCY FOR MODERATING VOICE COMMUNICATION
The abstract describes a computer-implemented method to determine whether to introduce latency into an audio stream based on the level of toxicity detected in the stream.
- The method involves analyzing an audio stream from a sender device, along with speech analysis scores, voice emotion parameters, and voice emotion scores for a user associated with the sender device.
- A trained machine-learning model is iteratively applied to the audio stream to generate a toxicity level output.
- The audio stream is then transmitted to a recipient device with a time delay introduced based on the toxicity level.
Potential Applications: - Online communication platforms to filter out toxic speech in real-time. - Call centers to monitor and manage customer interactions. - Educational platforms to provide feedback on the emotional tone of presentations.
Problems Solved: - Identifying and addressing toxic speech in audio streams. - Improving communication by introducing latency to prevent harmful interactions. - Enhancing user experience by promoting positive and respectful conversations.
Benefits: - Enhances online safety by filtering out toxic content. - Improves communication by promoting positive interactions. - Provides valuable insights into emotional aspects of speech.
Commercial Applications: Title: Real-time Toxicity Detection System for Audio Streams This technology can be utilized in social media platforms, online gaming communities, and customer service centers to enhance user experience and promote positive interactions. The market implications include improved user engagement, brand reputation management, and increased platform safety.
Prior Art: Prior art related to this technology may include research on speech analysis, emotion detection in audio streams, and machine learning models for toxicity detection in text and speech.
Frequently Updated Research: Researchers are continuously exploring new methods for improving toxicity detection in audio streams, enhancing machine learning models for emotion analysis, and developing real-time communication tools for online platforms.
Questions about Real-time Toxicity Detection System for Audio Streams: 1. How does the trained machine-learning model analyze voice emotion parameters in the audio stream? 2. What are the potential challenges in implementing this technology across different communication platforms?
Original Abstract Submitted
a computer-implemented method to determine whether to introduce latency into an audio stream from a particular speaker includes an audio stream from a sender device. the method further includes providing, as input to a trained machine-learning model, the audio stream and a speech analysis score, information about one or more voice emotion parameters, and one or more voice emotion scores for a first user associated with the sender device, wherein the trained machine-learning model is iteratively applied to the audio stream and wherein each iteration corresponds to a respective portion of the audio stream. the method further includes generating as output, with the trained machine-learning model, a level of toxicity in the audio stream. the method further includes transmitting the audio stream to a recipient device, wherein the transmitting is performed to introduce a time delay in the audio stream based on the level of toxicity.
