Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Speech and audio processing has become a cornerstone in modern human-computer interaction, enabling systems to understand, analyze, and generate audio signals. Applications span voice assistants, automatic speech recognition (ASR), speaker identification, audio event detection, and music analysis. This paper provides a comprehensive review of contemporary speech and audio processing algorithms, examining their foundations, advancements, and practical applications. Traditional signal processing methods, such as Fourier transforms and filter banks, are discussed alongside modern machine learning and deep learning approaches including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Additionally, challenges such as noise robustness, real-time processing, and resource-efficient deployment are highlighted. The review also presents comparative studies, illustrative figures, and performance metrics of different algorithms.

Keywords: Speech Processing, Audio Signal Processing, Machine Learning, Deep Learning, Fourier Transform, Spectrogram, Automatic Speech Recognition, Speaker Identification