Conversion of Acoustic Signal (Speech) Into Text By Digital Filter using Natural Language Processing
This work addresses speech recognition issues for general communication applications, but it appears incremental as it builds on existing methods like MFCC and HMM without claiming major breakthroughs.
The paper tackled speech recognition by developing an interface using a digital filter and NLP to convert speech to text, aiming to reduce technical errors like linguistic faults and gender recognition failures, though no concrete performance numbers are provided.
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.