A Comprehensive Study on Speech Processing Analysis on Embedded Systems

Speech Processing is a field of signal processing that focuses on analyzing, interpreting, and generating human speech using computers. It combines digital signal processing, machine learning, and artificial intelligence to enable machines to understand and respond to spoken language. Speech signals are first captured using microphones, then converted into digital form for processing. Various techniques such as filtering, feature extraction, and classification are applied to analyze speech patterns. Speech Processing plays a major role in modern applications like speech recognition, speaker identification, voice assistants, emotion detection, and medical diagnosis. Technologies like Mel-Frequency Cepstral Coefficients (MFCC), Hidden Markov Models (HMM), and Deep Neural Networks (DNN) are widely used for accurate speech analysis. This presentation discusses the fundamentals of speech processing, background work, problem definition, techniques used, results, and future improvements. The aim is to understand how speech signals are processed and how this technology improves human-computer interaction in real-time applications.