Speech Text Alignment

Speech text alignment focuses on precisely mapping the temporal relationship between spoken audio and its corresponding written transcription. Current research emphasizes developing robust models, often employing variational autoencoders (VAEs), transformers, and diffusion models, to achieve accurate alignment even with noisy or imperfect data, leveraging techniques like knowledge distillation and self-supervised learning. Improved alignment is crucial for enhancing various speech processing applications, including automatic speech recognition, text-to-speech synthesis, and multilingual voice processing, leading to more accurate and efficient systems.