Continuous Speech Recognition

Continuous speech recognition (CSR) aims to accurately transcribe spoken language, a complex task hindered by variations in accent, speaker characteristics, and background noise. Current research focuses on improving accuracy through advanced model architectures like transformers and hidden Markov models, incorporating contextual information (e.g., using bidirectional context for punctuation and leveraging linguistic features for improved segmentation), and exploring techniques like in-context learning for efficient adaptation to new dialects or speakers. These advancements are crucial for enhancing applications such as voice assistants, meeting transcription, and machine translation, ultimately bridging the gap between human and machine understanding of spoken language.