In this paper we show how we have achieved the state-of-the- art performance on the industry-standard NIST 2000 Hub5 En- glish evaluation set.

It is shown that the proposed dense LSTMs would provide more reliable performance as compared to the conventional, residual LSTMs as more LSTM layers are ...

Aug 10, 2018 · DenseN-ets are very deep, compact convolutional neural networks, which have demonstrated incredible improvements over the state-of-the-art ...

Recently, [10] have shown that these standard ASR tasks and benchmarks create an overly-optimistic and misleading view of the current state of the art.

Densely Connected Networks for Conversational Speech Recognition. Authors: Kyu Jeong Han, Akshay Chandrashekaran, Jungsuk Kim, Ian R. Lane; Authorids ...

Aug 17, 2023 · We combined three speech spectral features based on different frequency scales into a densely connected network (DenseNet) to accomplish sound classification ...

We explore densely connected LSTMs, inspired by the densely connected convolutional networks recently introduced for image classification tasks. We also propose ...

This paper proposes a model of Densely connected feedforward convolutional networks (DCFCN). The deep features are gradually extracted by dense connection ...

This paper presents three innovative deep learning models for English accent classification: Multi-task Pyramid Split Attention- Densely Convolutional Networks ...

One strategy to improve feature re-usage is to fuse both ResNets and densely connected CNNs (DenseNets). DenseNets, however, over-allocate parameters for ...