DNN HMM

DNN-HMM systems combine the strengths of deep neural networks (DNNs) and hidden Markov models (HMMs) for tasks like speech recognition and keyword spotting, aiming to improve accuracy and efficiency compared to using either approach alone. Current research focuses on optimizing DNN-HMM architectures, exploring hybrid models incorporating components like Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs), and investigating techniques like multi-task learning and data augmentation to address challenges in low-resource scenarios. These advancements are significant for improving the performance of speech-related technologies across diverse languages and noisy environments, impacting applications ranging from voice assistants to medical transcription.