Samsung electronics co., ltd. (20240161736). ELECTRONIC DEVICE AND METHOD OF LOW LATENCY SPEECH ENHANCEMENT USING AUTOREGRESSIVE CONDITIONING-BASED NEURAL NETWORK MODEL simplified abstract

ELECTRONIC DEVICE AND METHOD OF LOW LATENCY SPEECH ENHANCEMENT USING AUTOREGRESSIVE CONDITIONING-BASED NEURAL NETWORK MODEL

Organization Name

samsung electronics co., ltd.

Inventor(s)

Nikolas Andrew Babaev of Moscow (RU)

Pavel Konstantinovich Andreev of Moscow (RU)

Azat Rustamovich Saginbaev of Moscow (RU)

Ivan Sergeevich Shchekotov of Moscow (RU)

ELECTRONIC DEVICE AND METHOD OF LOW LATENCY SPEECH ENHANCEMENT USING AUTOREGRESSIVE CONDITIONING-BASED NEURAL NETWORK MODEL - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240161736 titled 'ELECTRONIC DEVICE AND METHOD OF LOW LATENCY SPEECH ENHANCEMENT USING AUTOREGRESSIVE CONDITIONING-BASED NEURAL NETWORK MODEL

Simplified Explanation

A neural method model is trained using teacher forcing and autoregressive channels, with predictions replacing ground-truth waveforms in subsequent training iterations.

• Neural network model trained using teacher forcing
• Autoregressive channel includes ground-truth shifted waveform
• Predictions of neural network model outputted
• Ground-truth waveform replaced with predictions in subsequent training iterations
• Inference performed using additional channel with predictions
• Speech enhancement performed using neural network model

Potential Applications

This technology can be applied in various fields such as speech recognition, audio processing, and signal enhancement.

Problems Solved

This technology helps in improving the accuracy and efficiency of speech enhancement systems by training neural networks using teacher forcing and autoregressive channels.

Benefits

The benefits of this technology include enhanced speech quality, improved noise reduction, and better overall performance of speech enhancement systems.

Potential Commercial Applications

Potential commercial applications of this technology include speech recognition software, audio editing tools, and communication devices with enhanced speech quality.

Possible Prior Art

One possible prior art for this technology could be the use of autoregressive models in speech enhancement systems to improve speech quality and reduce noise.

Unanswered Questions

How does this technology compare to traditional speech enhancement methods?

This article does not provide a direct comparison between this technology and traditional speech enhancement methods. Further research and experimentation may be needed to determine the advantages and limitations of this approach.

What are the computational requirements for implementing this technology in real-time applications?

The article does not address the computational requirements for real-time implementation of this technology. Understanding the computational resources needed for deploying this technology in real-time scenarios is crucial for practical applications.

Original Abstract Submitted

a neural method model is trained by, in an initial training iteration, training the neural network model in a teacher forcing mode in which an autoregressive channel includes a ground-truth shifted waveform, and outputting predictions of the neural network model; and in at least one additional training iteration, replacing the ground-truth shifted waveform in the autoregressive channel with the predictions of the neural network model obtained in a previous training iteration. an inference may then be performed by providing, for the neural network model, an additional channel containing at least one prediction of the neural network model outputted during training; and performing speech enhancement using the neural network model.