Hybrid Automatic Speech Recognition

Hybrid automatic speech recognition (ASR) systems aim to combine the strengths of traditional statistical models (like Hidden Markov Models) with the power of deep learning, seeking to improve accuracy and efficiency in speech-to-text conversion. Current research focuses on novel architectures such as transducers and the integration of large language models for error correction and improved context modeling, along with exploring techniques like data augmentation and model ensembling to address challenges such as gender bias and domain adaptation. These advancements are significant because they offer a path towards more robust and accurate ASR systems applicable to diverse real-world scenarios, including resource-constrained environments and conversational speech.