title: Dynamic Chunk Convolution for Unified Streaming and Non-Streaming Conformer ASR
abstract: Recently, there has been an increasing interest in unifying streaming and non-streaming speech recognition models to reduce development, training and deployment cost. The best-known approaches rely on either window-based or dynamic chunk-based attention strategy and causal convolutions to minimize the degradation due to streaming. However, the performance gap still remains relatively large between non-streaming and a full-contextual model trained independently. To address this, we propose a dyna
journal: arXiv (Cornell University)