Speech Recognition Using Markov Models

This text discusses speech recognition technology utilizing Markov models. A Markov model is a statistical framework that characterizes stochastic processes with Markov properties, where future states depend only on the current state. Speech recognition involves converting human speech into text or commands. Markov model-based speech recognition leverages the model's probabilistic structure to analyze and model audio signal distributions for decoding spoken content. Key implementations often involve:

- Hidden Markov Models (HMMs) where states represent phonemes or acoustic units

- Feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs) for audio preprocessing

- Viterbi algorithm for finding the most probable state sequence

- Baum-Welch algorithm for model parameter training

This technology is widely applied in systems like voice assistants, speech-controlled interfaces, and transcription services. Significant advancements in accuracy and performance have established Markov model-based approaches as fundamental research directions in modern speech recognition.