Hidden Markov Models (HMMs) are widely used in speech recognition due to their ability to model time-varying spectral sequences. Modern large vocabulary continuous speech recognition (LVCSR) systems rely heavily on HMMs, though direct implementation can lead to poor accuracy and sensitivity to environmental changes. Practical applications involve sophisticated refinements such as feature projection, improved covariance modeling, discriminative parameter estimation, adaptation, normalization, noise compensation, and multi-pass system combination. The review discusses these techniques and presents a case study on broadcast news and conversation transcription. The core of speech recognition systems is a statistical model of language sounds. HMMs provide a natural framework for modeling these sounds, especially since speech has temporal structure. HMMs are central to modern speech recognition systems, with significant advancements in modeling techniques over the past decade. The foundations of HMM-based speech recognition were laid in the 1970s, with the introduction of discrete and continuous density HMMs. Early systems focused on discrete word speaker-dependent systems or whole word small vocabulary systems. By the early 1990s, attention shifted to continuous speaker-independent recognition. The development of HMMs has led to significant progress in speech recognition, with techniques such as feature extraction, HMM acoustic models, N-gram language models, and decoding algorithms being refined. Feature extraction involves transforming speech waveforms into compact representations, often using mel-frequency cepstral coefficients (MFCCs) or perceptual linear prediction (PLP). HMM acoustic models use continuous density HMMs with Gaussian output distributions. These models are refined through techniques like triphone modeling, state tying, and decision tree clustering to handle context-dependent variations in speech. N-gram language models are used to estimate the prior probability of word sequences, with techniques like Katz smoothing and class-based models to address data sparsity. Decoding algorithms, such as the Viterbi algorithm, are used to find the most likely word sequence, with lattice generation for efficient hypothesis management. HMM structure refinements include Gaussian mixture models, efficient covariance modeling, and feature projection schemes. These techniques improve the performance of speech recognition systems by handling complex and larger vocabulary tasks. Structured covariance matrices and precision matrix representations are used to improve covariance modeling with minimal computational overhead. Feature projections, such as principal component analysis (PCA) and linear discriminant analysis (LDA), are used to decorrelate features and reduce dimensionality, enhancing the diagonal covariance approximation.Hidden Markov Models (HMMs) are widely used in speech recognition due to their ability to model time-varying spectral sequences. Modern large vocabulary continuous speech recognition (LVCSR) systems rely heavily on HMMs, though direct implementation can lead to poor accuracy and sensitivity to environmental changes. Practical applications involve sophisticated refinements such as feature projection, improved covariance modeling, discriminative parameter estimation, adaptation, normalization, noise compensation, and multi-pass system combination. The review discusses these techniques and presents a case study on broadcast news and conversation transcription. The core of speech recognition systems is a statistical model of language sounds. HMMs provide a natural framework for modeling these sounds, especially since speech has temporal structure. HMMs are central to modern speech recognition systems, with significant advancements in modeling techniques over the past decade. The foundations of HMM-based speech recognition were laid in the 1970s, with the introduction of discrete and continuous density HMMs. Early systems focused on discrete word speaker-dependent systems or whole word small vocabulary systems. By the early 1990s, attention shifted to continuous speaker-independent recognition. The development of HMMs has led to significant progress in speech recognition, with techniques such as feature extraction, HMM acoustic models, N-gram language models, and decoding algorithms being refined. Feature extraction involves transforming speech waveforms into compact representations, often using mel-frequency cepstral coefficients (MFCCs) or perceptual linear prediction (PLP). HMM acoustic models use continuous density HMMs with Gaussian output distributions. These models are refined through techniques like triphone modeling, state tying, and decision tree clustering to handle context-dependent variations in speech. N-gram language models are used to estimate the prior probability of word sequences, with techniques like Katz smoothing and class-based models to address data sparsity. Decoding algorithms, such as the Viterbi algorithm, are used to find the most likely word sequence, with lattice generation for efficient hypothesis management. HMM structure refinements include Gaussian mixture models, efficient covariance modeling, and feature projection schemes. These techniques improve the performance of speech recognition systems by handling complex and larger vocabulary tasks. Structured covariance matrices and precision matrix representations are used to improve covariance modeling with minimal computational overhead. Feature projections, such as principal component analysis (PCA) and linear discriminant analysis (LDA), are used to decorrelate features and reduce dimensionality, enhancing the diagonal covariance approximation.