50 citations · 92 across the 13 of their papers we have counts for
13 papers
Hybrid Continual Learning for Low-Resource Australian Aboriginal Language Identification
Pravina Mylvaganam, Ting Dang, Eliathamby Ambikairajah +2
Language identification is an important step toward integrating endangered Australian Aboriginal languages (AALs) into speech technologies supporting language revitalisation and di…
Which Languages Transfer Best to Warlpiri? A Similarity-Based Study for Low-Resource ASR
Pravina Mylvaganam, Eliathamby Ambikairajah, Ting Dang +2
This paper investigates how language similarity can improve cross-lingual transfer for automatic speech recognition (ASR) in extremely low-resource settings. Warlpiri, an Australia…
Characterization of Speech Similarity Between Australian Aboriginal and High-Resource Languages: A Case Study on Dharawal
Ting Dang, Trini Manoj Jeyaseelan, Eliathamby Ambikairajah +1
Australian Aboriginal languages are of significant cultural and linguistic value but remain severely underrepresented in modern speech AI systems. While state-of-the-art speech fou…
A Joint Spectro-Temporal Relational Thinking Based Acoustic Modeling Framework
Zheng Nan, Ting Dang, Vidhyasaharan Sethu +1
Relational thinking refers to the inherent ability of humans to form mental impressions about relations between sensory signals and prior knowledge, and subsequently incorporate th…
AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models
Xin Hong, Yuan Gong, Vidhyasaharan Sethu +1
Recent advancements in Large Language Models (LLMs) have demonstrated great success in many Natural Language Processing (NLP) tasks. In addition to their cognitive intelligence, ex…
Dual-Constrained Dynamical Neural ODEs for Ambiguity-aware Continuous Emotion Prediction
Jingyao Wu, Ting Dang, Vidhyasaharan Sethu +1
There has been a significant focus on modelling emotion ambiguity in recent years, with advancements made in representing emotions as distributions to capture ambiguity. However, t…
Variational Connectionist Temporal Classification for Order-Preserving Sequence Modeling
Zheng Nan, Ting Dang, Vidhyasaharan Sethu +1
Connectionist temporal classification (CTC) is commonly adopted for sequence modeling tasks like speech recognition, where it is necessary to preserve order between the input and t…
Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
Ting Dang, Jing Han, Tong Xia +9
Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection a…
Joint Spatio-Temporal Discretisation of Nonlinear Active Cochlear Models
T. Dang, V. Sethu, E. Ambikairajah +2
Biologically inspired auditory models play an important role in developing effective audio representations that can be tightly integrated into speech and audio processing systems.…
A Novel Markovian Framework for Integrating Absolute and Relative Ordinal Emotion Information
Jingyao Wu, Ting Dang, Vidhyasaharan Sethu +1
There is growing interest in affective computing for the representation and prediction of emotions along ordinal scales. However, the term ordinal emotion label has been used to re…
hEARt: Motion-resilient Heart Rate Monitoring with In-ear Microphones
Kayla-Jade Butkow, Ting Dang, Andrea Ferlini +2
With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate (HR) detection systems. HR is a key physiological marker…
Sounds of COVID-19: exploring realistic performance of audio-based digital testing
Jing Han, Tong Xia, Dimitris Spathis +9
Researchers have been battling with the question of how we can identify Coronavirus disease (COVID-19) cases efficiently, affordably and at scale. Recent work has shown how audio b…
Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data
Tong Xia, Jing Han, Lorena Qendro +2
Recently, sound-based COVID-19 detection studies have shown great promise to achieve scalable and prompt digital pre-screening. However, there are still two unsolved issues hinderi…