Connectionist Temporal Classification

Connectionist Temporal Classification (CTC) is a widely used technique in sequence modeling, particularly for automatic speech recognition (ASR), aiming to efficiently align input sequences (e.g., audio spectrograms) with output sequences (e.g., text transcriptions) without requiring explicit frame-level alignment. Current research focuses on improving CTC's performance and efficiency through methods like consistency regularization, speaker-aware training, and encoder prompting, often integrated with transformer architectures or hybrid CTC/attention-based models. These advancements are significantly impacting ASR applications by enhancing accuracy, speed, and robustness, particularly in challenging scenarios such as multi-talker speech, low-resource languages, and code-switching.