6 citations · 11 across the 7 of their papers we have counts for
7 papers
Oh, Jeez! or Uh-huh? A Listener-aware Backchannel Predictor on ASR Transcriptions
Daniel Ortega, Chia-Yu Li, Ngoc Thang Vu
This paper presents our latest investigation on modeling backchannel in conversations. Motivated by a proactive backchanneling theory, we aim at developing a system which acts as a…
Integrating Knowledge in End-to-End Automatic Speech Recognition for Mandarin-English Code-Switching
Chia-Yu Li, Ngoc Thang Vu
Code-Switching (CS) is a common linguistic phenomenon in multilingual communities that consists of switching between languages while speaking. This paper presents our investigation…
Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks
Chia-Yu Li, Ngoc Thang Vu
This paper presents our latest effort on improving Code-switching language models that suffer from data scarcity. We investigate methods to augment Code-switching training text dat…
Improving Speech Recognition on Noisy Speech via Speech Enhancement with Multi-Discriminators CycleGAN
Chia-Yu Li, Ngoc Thang Vu
This paper presents our latest investigations on improving automatic speech recognition for noisy speech via speech enhancement. We propose a novel method named Multi-discriminator…
ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents
Chia-Yu Li, Daniel Ortega, Dirk Väth +9
We present ADVISER - an open-source, multi-domain dialog system toolkit that enables the development of multi-modal (incorporating speech, text and vision), socially-engaged (e.g.…
Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions
Daniel Ortega, Chia-Yu Li, Gisela Vallejo +2
This paper presents our latest investigations on dialog act (DA) classification on automatically generated transcriptions. We propose a novel approach that combines convolutional n…
Densely Connected Convolutional Networks for Speech Recognition
Chia Yu Li, Ngoc Thang Vu
This paper presents our latest investigation on Densely Connected Convolutional Networks (DenseNets) for acoustic modelling (AM) in automatic speech recognition. DenseN-ets are ver…