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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm.

Akmalbek Bobomirzaevich Abdusalomov¹, Furkat Safarov¹, Mekhriddin Rakhimov²

¹Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Korea.

Sensors (Basel, Switzerland)

|November 11, 2022

Summary

This summary is machine-generated.

This study introduces a machine learning approach for faster speech recognition feature extraction, crucial for real-time smart city applications. The method enhances processing speed and classification accuracy compared to traditional algorithms.

Keywords:

distributed computing feature extraction multicore processor parallel computing spectral analysis speech recognition

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Area of Science:

Computer Science
Signal Processing

Background:

Speech recognition involves acoustic processing, feature extraction, and classification.
Efficient feature extraction is vital for handling complex acoustic signals in real-time systems.
Current methods face challenges in processing speed and overclocking during digital signal processing.

Purpose of the Study:

To propose a machine learning-based approach for feature parameter extraction in speech signals.
To enhance the performance of speech recognition in real-time smart city environments.
To reduce computing time using cache memory mapping principles.

Main Methods:

Utilizing a machine learning-based approach for feature parameter extraction.
Implementing cache memory mapping to optimize computing time.
Focusing on accelerating the extraction of feature parameters from speech signals.

Main Results:

The proposed method successfully extracts relevant speech signal features.
Achieved seamless classification performance compared to conventional algorithms.
Demonstrated improved processing speed for real-time applications.

Conclusions:

The machine learning approach effectively improves speech recognition performance.
Cache memory optimization contributes to reduced computing time.
The method shows promise for real-time smart city speech recognition applications.