Mitsubishi Electric Research Laboratories, Inc. (20240304205). System and Method for Audio Processing using Time-Invariant Speaker Embeddings simplified abstract

System and Method for Audio Processing using Time-Invariant Speaker Embeddings

Organization Name

Mitsubishi Electric Research Laboratories, Inc.

Inventor(s)

Aswin Shanmugam Subramanian of Everett MA (US)

[[:Category:Christoph B�ddeker of Paderborn (DK)|Christoph B�ddeker of Paderborn (DK)]][[Category:Christoph B�ddeker of Paderborn (DK)]]

Gordon Wichern of Boston MA (US)

Jonathan Le Roux of Arlington MA (US)

System and Method for Audio Processing using Time-Invariant Speaker Embeddings - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240304205 titled 'System and Method for Audio Processing using Time-Invariant Speaker Embeddings

Simplified Explanation: The patent application describes a system and method for analyzing multi-talker conversations using sound processing techniques.

• The system includes a deep neural network trained to process audio segments from a mixture of voices in a conversation.
• It uses a speaker-independent layer to generate speaker-independent output and a speaker-biased layer to process each audio segment for each speaker in the conversation.
• The network assigns time-invariant embeddings to individual speakers to identify their time-frequency activity regions in the audio mixture.

Key Features and Innovation:

• Utilizes a deep neural network for analyzing multi-talker conversations.
• Incorporates speaker-independent and speaker-biased layers for accurate speaker identification.
• Processes time-invariant embeddings to track each speaker's activity in the conversation.

Potential Applications:

• Speech recognition systems
• Meeting transcription tools
• Call center analytics

Problems Solved:

• Difficulty in analyzing multi-talker conversations
• Speaker identification challenges in audio mixtures

Benefits:

• Enhanced accuracy in speaker identification
• Improved understanding of multi-talker conversations
• Efficient processing of audio mixtures

Commercial Applications: The technology can be used in call centers for analyzing customer interactions, in meeting transcription services, and in speech recognition software for improved performance.

Questions about Multi-Talker Conversation Analysis: 1. How does the deep neural network differentiate between multiple speakers in a conversation? 2. What are the potential challenges in implementing this technology in real-time communication systems?

Frequently Updated Research: Researchers are constantly exploring ways to improve speaker separation and speech recognition accuracy in multi-talker conversations using deep learning techniques.

Original Abstract Submitted

a system and method for sound processing for performing multi-talker conversation analysis is provided. the sound processing system includes a deep neural network trained for processing audio segments of an audio mixture of the multi-talker conversation. the deep neural network includes a speaker-independent layer that produces a speaker-independent output, and a speaker-biased layer applied once independently to each of the audio segments for each multiple speakers of the audio mixture. the deep neural network also processes a time-invariant embedding by individually assigning each application of the speaker-biased layer to a corresponding speaker by inputting the corresponding time-invariant speaker embedding. the deep neural network thus produces data indicative of time-frequency activity regions of each speaker of the multiple speakers in the audio mixture from a combination of speaker-biased outputs.