17962248. SPEAKER IDENTIFICATION, VERIFICATION, AND DIARIZATION USING NEURAL NETWORKS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS simplified abstract (NVIDIA Corporation)

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 17962248 titled 'SPEAKER IDENTIFICATION, VERIFICATION, AND DIARIZATION USING NEURAL NETWORKS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

Simplified Explanation

The patent application describes the use of machine learning, specifically a neural network, for speaker recognition, verification, and diarization based on speech data.

• Neural network (NN) used to obtain speaker embeddings from speech data
• Speech data includes spectral content represented by frames and channels
• NN includes branches for convolutions across channels and frames
• Speaker embeddings can be used for speaker identification, verification, and diarization

Potential Applications

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

Problems Solved

The technology solves the challenges of accurately identifying speakers, verifying their identity, and diarizing multiple speakers in audio recordings.

Benefits

The benefits of this technology include improved accuracy in speaker recognition, enhanced security measures, efficient organization of audio data, and streamlined voice-controlled applications.

Potential Commercial Applications

The technology can be commercialized in industries such as security, telecommunications, customer service, law enforcement, and smart home devices.

Possible Prior Art

Prior art in speaker recognition and verification includes traditional methods such as Gaussian Mixture Models (GMM) and Support Vector Machines (SVM) which may not be as effective as machine learning approaches like neural networks.

How does this technology compare to traditional speaker recognition methods like GMM and SVM?

The article does not provide a direct comparison between the proposed technology and traditional methods like GMM and SVM in terms of accuracy, efficiency, and scalability.

What are the potential limitations or challenges in implementing this technology in real-world applications?

The article does not address the potential limitations or challenges that may arise when implementing this technology in real-world applications, such as data privacy concerns, computational resources required, or adaptability to different languages and accents.

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.