Nvidia corporation (20240119927). SPEAKER IDENTIFICATION, VERIFICATION, AND DIARIZATION USING NEURAL NETWORKS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS simplified abstract

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SPEAKER IDENTIFICATION, VERIFICATION, AND DIARIZATION USING NEURAL NETWORKS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

Organization Name

nvidia corporation

Inventor(s)

Nithin Rao Koluguri of San Jose CA (US)

Taejin Park of San Jose CA (US)

Boris Ginsburg of Sunnyvale CA (US)

SPEAKER IDENTIFICATION, VERIFICATION, AND DIARIZATION USING NEURAL NETWORKS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240119927 titled 'SPEAKER IDENTIFICATION, VERIFICATION, AND DIARIZATION USING NEURAL NETWORKS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

Simplified Explanation

The patent application describes techniques for implementing speaker recognition, verification, and/or diarization using machine learning, specifically a neural network.

  • The neural network processes speech data to obtain speaker embeddings representing the association between the speech data and the speaker.
  • The speech data consists of frames and channels representing spectral content.
  • The neural network includes branches for convolutions across channels and frames to extract speaker embeddings.
  • The obtained speaker embeddings can be used for speaker identification, verification, and diarization tasks.

Potential Applications

The technology can be applied in various fields such as security systems, voice-controlled devices, and call center authentication.

Problems Solved

This technology addresses the challenges of accurately identifying speakers in noisy environments and distinguishing between different speakers with similar voices.

Benefits

The benefits of this technology include improved accuracy in speaker recognition, enhanced security measures, and streamlined authentication processes.

Potential Commercial Applications

Potential commercial applications include voice-controlled smart home devices, secure access systems, and customer service call centers.

Possible Prior Art

One possible prior art could be traditional speaker recognition systems that rely on manual feature extraction and matching techniques.

Unanswered Questions

How does this technology handle variations in speech patterns due to accents or speech impediments?

The patent application does not specifically address how the neural network adapts to variations in speech patterns caused by accents or speech impediments.

What is the computational efficiency of the neural network when processing large amounts of speech data?

The patent application does not provide information on the computational efficiency of the neural network when dealing with significant volumes of speech data.


Original Abstract Submitted

disclosed are apparatuses, systems, and techniques that may use machine learning for implementing speaker recognition, verification, and/or diarization. the techniques include applying a neural network (nn) to a speech data to obtain a speaker embedding representative of an association between the speech data and a speaker that produced the speech. the speech data includes a plurality of frames and a plurality of channels representative of spectral content of the speech data. the nn has one or more blocks of neurons that include a first branch performing convolutions of the speech data across the plurality of channels and across the plurality of frames and a second branch performing convolutions of the speech data across the plurality of channels. obtained speaker embeddings may be used for various tasks of speaker identification, verification, and/or diarization.

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