70 citations · 180 across the 53 of their papers we have counts for
11 papers · 1 filter
Investigation of Densely Connected Convolutional Networks with Domain Adversarial Learning for Noise Robust Speech Recognition
Chia Yu Li, Ngoc Thang Vu
We investigate densely connected convolutional networks (DenseNets) and their extension with domain adversarial training for noise robust speech recognition. DenseNets are very dee…
Predicting User Code-Switching Level from Sociological and Psychological Profiles
Injy Hamed, Alia El Bolock, Nader Rizk +3
Multilingual speakers tend to alternate between languages within a conversation, a phenomenon referred to as "code-switching" (CS). CS is a complex phenomenon that not only encompa…
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…
Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings
Hendrik Schuff, Hsiu-Yu Yang, Heike Adel +1
Natural language inference (NLI) requires models to learn and apply commonsense knowledge. These reasoning abilities are particularly important for explainable NLI systems that gen…
Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech
Injy Hamed, Pavel Denisov, Chia-Yu Li +3
Code-switching (CS), defined as the mixing of languages in conversations, has become a worldwide phenomenon. The prevalence of CS has been recently met with a growing demand and in…
Thought Flow Nets: From Single Predictions to Trains of Model Thought
Hendrik Schuff, Heike Adel, Ngoc Thang Vu
When humans solve complex problems, they typically create a sequence of ideas (involving an intuitive decision, reflection, error correction, etc.) in order to reach a conclusive d…
IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task
Pavel Denisov, Manuel Mager, Ngoc Thang Vu
This paper describes the submission to the IWSLT 2021 Low-Resource Speech Translation Shared Task by IMS team. We utilize state-of-the-art models combined with several data augment…
Few-shot Learning for Slot Tagging with Attentive Relational Network
Cennet Oguz, Ngoc Thang Vu
Metric-based learning is a well-known family of methods for few-shot learning, especially in computer vision. Recently, they have been used in many natural language processing appl…
Investigations on Audiovisual Emotion Recognition in Noisy Conditions
Michael Neumann, Ngoc Thang Vu
In this paper we explore audiovisual emotion recognition under noisy acoustic conditions with a focus on speech features. We attempt to answer the following research questions: (i)…
Meta-Learning for improving rare word recognition in end-to-end ASR
Florian Lux, Ngoc Thang Vu
We propose a new method of generating meaningful embeddings for speech, changes to four commonly used meta learning approaches to enable them to perform keyword spotting in continu…