Conversion of Acoustic Signal (Speech) Into Text By Digital Filter using Natural Language Processing

TL;DR

This paper presents a digital filter-based speech-to-text conversion system utilizing natural language processing, aiming to improve accuracy and reduce technical errors in speech recognition applications.

Contribution

It introduces a novel interface combining digital filtering with NLP techniques, addressing common issues like gender recognition failures and technical errors in speech recognition.

Findings

Reduced technical errors in speech-to-text conversion

Improved accuracy with MFCC and HMM integration

Enhanced robustness against linguistic faults

Abstract

One of the most crucial aspects of communication in daily life is speech recognition. Speech recognition that is based on natural language processing is one of the essential elements in the conversion of one system to another. In this paper, we created an interface that transforms speech and other auditory inputs into text using a digital filter. Contrary to the many methods for this conversion, it is also possible for linguistic faults to appear occasionally, gender recognition, speech recognition that is unsuccessful (cannot recognize voice), and gender recognition to fail. Since technical problems are involved, we developed a program that acts as a mediator to prevent initiating software issues in order to eliminate even this little deviation. Its planned MFCC and HMM are in sync with its AI system. As a result, technical errors have been avoided.