Neural Network Audio Filter for Speech Impaired Messages
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Solution Overview
Problem
Individuals with speech impairments face challenges in effectively communicating through audio communication platforms, as existing technologies lack efficient solutions to correct distorted or repetitive speech.
Innovation Solution
A method utilizing a neural network (NN) audio filter is implemented to identify and remove impaired sections from speech impaired messages, converting the audio signal into characters, and removing repetitive patterns or duplicate words to form an unimpaired message.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional audio communication platforms are used, then communication between individuals is enabled, but speech impaired messages remain distorted or repetitive and difficult to understand
Solution Approach 1:
A neural network audio filter is introduced as an intermediary component between the speech input and output. The filter processes impaired speech signals by identifying and removing repetitive patterns and distorted sections, converting them into clearer unimpaired messages while maintaining the original communication intent
Solution Approach 2:
Traditional signal processing methods are replaced with a neural network-based system that uses machine learning algorithms to automatically detect and correct speech impairments. The neural network learns patterns of distorted speech and applies intelligent transformations to restore clarity
2Measurement precision
If speech impaired messages are processed to remove impaired sections, then message clarity is improved, but processing complexity increases
Solution Approach 1:
The neural network filter extracts and removes specific impaired sections from the audio signal by identifying repetitive patterns and distorted segments. The system isolates problematic portions and eliminates them to produce a cleaner output message
Solution Approach 2:
The audio signal is divided into discrete segments or time steps that are processed individually by the neural network. This segmentation allows the system to analyze and correct specific portions of speech without overwhelming the processing system
3Speed
If real-time processing of speech impaired messages is implemented, then communication responsiveness is improved, but computational requirements increase
Solution Approach 1:
The neural network filter is pre-trained on large datasets of speech patterns before deployment. This preliminary training enables the system to quickly recognize and correct speech impairments in real-time without requiring extensive computational resources during actual processing
Data Source
AI summary
A computer implemented method, system and computer program product are provided that implement a neural network (NN) audio filter. The method, system and computer program product obtain an electronic audio signal comprising a speech impaired message and apply the audio signal to the NN audio filter to modify the speech impaired message. The apply operation converts the audio signal into characters; and identifies and removes at least one of a repetitive pattern or duplicate word in the characters to form the unimpaired message. The unimpaired message is output.