3 accepted papers
Multimodal learning is provably superior to unimodal learning. However, in practice, the best-performing unimodal networks often outperform jointly trained multimodal networks. This phenomenon can be attributed to the varying convergence and generalization rates across different modalities, leading…
RNN-T models are widely used in ASR, which rely on the RNN-T loss to achieve length alignment between input audio and target sequence. However, the implementation complexity and the alignment-based optimization target of RNN-T loss lead to computational redundancy and a reduced role for predictor ne…
Transformer recently has achieved impressive success in a number of domains, including machine translation, image recognition, and speech recognition. Most of the previous work on Keyword Spotting (KWS) is built upon convolutional or recurrent neural networks. In this paper, we explore a family of T…