Sequence Discriminative Training

Sequence discriminative training aims to improve the performance of sequence models, such as those used in automatic speech recognition (ASR), by directly optimizing the model's ability to distinguish between correct and incorrect sequences. Current research focuses on applying this technique within various architectures, including neural transducers and transformers, often incorporating language models to refine the training process and exploring both lattice-free and N-best list approaches. These advancements lead to significant improvements in accuracy and efficiency for tasks like ASR and keyword spotting, impacting the development of more robust and resource-efficient speech processing systems.