4 citations · 4 across the 2 of their papers we have counts for
5 papers
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…
Low-Resource Contextual Topic Identification on Speech
Chunxi Liu, Matthew Wiesner, Shinji Watanabe +4
In topic identification (topic ID) on real-world unstructured audio, an audio instance of variable topic shifts is first broken into sequential segments, and each segment is indepe…
Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages
Matthew Wiesner, Chunxi Liu, Lucas Ondel +6
Automatic speech recognition (ASR) systems often need to be developed for extremely low-resource languages to serve end-uses such as audio content categorization and search. While…
An Empirical Evaluation of Zero Resource Acoustic Unit Discovery
Chunxi Liu, Jinyi Yang, Ming Sun +7
Acoustic unit discovery (AUD) is a process of automatically identifying a categorical acoustic unit inventory from speech and producing corresponding acoustic unit tokenizations. A…