261 citations · 439 across the 15 of their papers we have counts for
4 papers · 1 filter
From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition
Duc Le, Xiaohui Zhang, Weiyi Zheng +3
There is an implicit assumption that traditional hybrid approaches for automatic speech recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to g…
Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks
Andros Tjandra, Chunxi Liu, Frank Zhang +5
Deep acoustic models typically receive features in the first layer of the network, and process increasingly abstract representations in the subsequent layers. Here, we propose to f…
Transformer-based Acoustic Modeling for Hybrid Speech Recognition
Yongqiang Wang, Abdelrahman Mohamed, Duc Le +10
We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech recognition. Several modeling choices are discussed in this work, including various positional emb…
Multilingual Graphemic Hybrid ASR with Massive Data Augmentation
Chunxi Liu, Qiaochu Zhang, Xiaohui Zhang +3
Towards developing high-performing ASR for low-resource languages, approaches to address the lack of resources are to make use of data from multiple languages, and to augment the t…