Deep neural networks (DNNs) for speech recognition are a key research area in artificial intelligence and signal processing, focusing on building models that convert spoken language into text or actionable representations. Research papers in this domain explore architectures such as feedforward DNNs, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), gated recurrent units (GRUs), and transformer-based models (e.g., Wav2Vec, Conformer) to capture temporal and spectral features of audio signals. Key contributions include acoustic modeling, feature extraction with Mel-frequency cepstral coefficients (MFCCs) or spectrograms, sequence-to-sequence learning, attention mechanisms, and end-to-end speech recognition frameworks. Recent studies also address challenges such as noise robustness, speaker variability, real-time processing, low-resource languages, multilingual speech recognition, and deployment on edge and mobile devices. By leveraging deep neural networks, research in speech recognition aims to achieve high-accuracy, low-latency, and scalable systems applicable in virtual assistants, transcription services, healthcare, and human-computer interaction.