Attention-Enhanced Neural Networks for Accurate Speech Processing

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Solution Overview

Problem

Current speech recognition technologies are prone to failures in converting audio signals to text and can be improved for better accuracy and robustness.

Innovation Solution

The use of attention-enhanced deep convolutional neural networks and transformer architectures to identify features of audio signals, generate text, and create graphical representations of speech, incorporating self-attention and multi-headed attention mechanisms to enhance contextual understanding and accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If current speech recognition techniques are used to convert audio signals to text, then the system is simple and easy to implement, but the accuracy and reliability of speech-to-text conversion deteriorates

Engineering Contradiction:

Improvespeech-to-text conversion accuracyVSAvoidneural network architecture complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The audio signal is divided into multiple time periods or frames, and the neural network processes each segment independently to identify features. This segmentation allows the system to handle complex speech data in manageable portions, improving accuracy without overwhelming the computational system

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces temporal dimension by using features from multiple time periods to generate text. Instead of processing audio in a single snapshot, the system incorporates temporal context by utilizing features across different time frames, thereby improving speech recognition accuracy through dimensional expansion

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Loss of information

If features from one time period are used to generate text for the same time period only, then the processing is straightforward, but the contextual understanding deteriorates

Engineering Contradiction:

Improvecontextual information retentionVSAvoidfeature relationship processing

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The neural network performs preliminary feature extraction from multiple time periods before generating the final text output. By pre-identifying and storing features from surrounding time periods, the system prepares contextual information in advance, ensuring that when text generation occurs, rich contextual data is already available

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediate representation layer where audio features from multiple time periods are transformed and integrated before producing the final text. This intermediary processing stage acts as a mediator that combines temporal features and contextual information, preserving information that would otherwise be lost in direct conversion

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250252951A1Speech processing technique

Publication Date: 2025.08.07 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques to generate text from an audio signal. In at least one embodiment, one or more neural networks are used to generate text from an audio signal, wherein the one or more neural networks comprise one or more portions to each identify one or more features of a corresponding time period of the audio signal to be used to generate text corresponding to one or more other time periods of the audio signal.