PINDROP SECURITY, INC. (20240233709). SYSTEMS AND METHODS OF SPEAKER-INDEPENDENT EMBEDDING FOR IDENTIFICATION AND VERIFICATION FROM AUDIO simplified abstract
Contents
- 1 SYSTEMS AND METHODS OF SPEAKER-INDEPENDENT EMBEDDING FOR IDENTIFICATION AND VERIFICATION FROM AUDIO
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS OF SPEAKER-INDEPENDENT EMBEDDING FOR IDENTIFICATION AND VERIFICATION FROM AUDIO - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Audio Processing
- 1.11 Frequently Updated Research
- 1.12 Original Abstract Submitted
SYSTEMS AND METHODS OF SPEAKER-INDEPENDENT EMBEDDING FOR IDENTIFICATION AND VERIFICATION FROM AUDIO
Organization Name
Inventor(s)
Kedar Phatak of Atlanta GA (US)
Elie Khoury of Atlanta GA (US)
SYSTEMS AND METHODS OF SPEAKER-INDEPENDENT EMBEDDING FOR IDENTIFICATION AND VERIFICATION FROM AUDIO - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240233709 titled 'SYSTEMS AND METHODS OF SPEAKER-INDEPENDENT EMBEDDING FOR IDENTIFICATION AND VERIFICATION FROM AUDIO
Simplified Explanation
The patent application describes a method for processing audio signals to evaluate characteristics that are independent of the speaker's voice. A neural network architecture is used to train discriminatory neural networks that model and classify these speaker-independent characteristics. The resulting deep-phoneprint vector represents these characteristics in a low-dimensional form for downstream operations.
- Evaluates characteristics of audio signals independent of the speaker's voice
- Uses a neural network architecture to train discriminatory neural networks
- Generates feature vectors from input audio data
- Concatenates embeddings from task-specific models to form a deep-phoneprint vector
- Represents speaker-independent characteristics in a low-dimensional form
Key Features and Innovation
The innovation lies in the use of neural networks to process audio signals and extract speaker-independent characteristics efficiently and effectively.
Potential Applications
- Speech recognition systems
- Speaker identification technology
- Audio content analysis tools
Problems Solved
This technology addresses the challenge of accurately evaluating audio signals based on characteristics that are not related to the speaker's voice.
Benefits
- Improved accuracy in audio processing
- Enhanced speaker-independent feature extraction
- Efficient downstream operations
Commercial Applications
- This technology could be applied in speech recognition software for improved accuracy and performance.
- It could also be used in security systems for speaker identification and verification.
Questions about Audio Processing
How does this technology improve upon existing methods of audio signal processing?
This technology utilizes neural networks to extract speaker-independent characteristics from audio signals, leading to more accurate and efficient processing.
What are the potential limitations of using deep-phoneprint vectors in audio analysis?
Deep-phoneprint vectors may have limitations in handling complex audio signals with multiple speakers or background noise.
Frequently Updated Research
Research on improving neural network architectures for audio signal processing and feature extraction is ongoing in the field of machine learning and signal processing.
Original Abstract Submitted
embodiments described herein provide for audio processing operations that evaluate characteristics of audio signals that are independent of the speaker's voice. a neural network architecture trains and applies discriminatory neural networks tasked with modeling and classifying speaker-independent characteristics. the task-specific models generate or extract feature vectors from input audio data based on the trained embedding extraction models. the embeddings from the task-specific models are concatenated to form a deep-phoneprint vector for the input audio signal. the dp vector is a low dimensional representation of the each of the speaker-independent characteristics of the audio signal and applied in various downstream operations.
