6 citations · 17 across the 17 of their papers we have counts for
18 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…
BiEAR: A Human Auditory-Inspired Adaptive Binaural Front-end for Multi-Speaker Localisation and Distance Estimation
Hanyu Meng, Eliathamby Ambikairajah, Vidhyasaharan Sethu +2
We present BiEAR, a human auditory-inspired adaptive binaural front-end for multi-speaker localisation and distance estimation. Inspired by medial olivocochlear (MOC) feedback in h…
Continual Adaptation for Pacific Indigenous Speech Recognition
Yang Xiao, Aso Mahmudi, Nick Thieberger +3
Speech foundation models struggle with low-resource Pacific Indigenous languages because of severe data scarcity. Furthermore, full fine-tuning risks catastrophic forgetting. To ad…
Adaptive Per-Channel Energy Normalization Front-end for Robust Audio Signal Processing
Hanyu Meng, Vidhyasaharan Sethu, Eliathamby Ambikairajah +2
In audio signal processing, learnable front-ends have shown strong performance across diverse tasks by optimizing task-specific representation. However, their parameters remain fix…
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…
Exploring Length Generalization For Transformer-based Speech Enhancement
Qiquan Zhang, Hongxu Zhu, Xinyuan Qian +2
Transformer network architecture has proven effective in speech enhancement. However, as its core module, self-attention suffers from quadratic complexity, making it infeasible for…
Should Audio Front-ends be Adaptive? Comparing Learnable and Adaptive Front-ends
Qiquan Zhang, Buddhi Wickramasinghe, Eliathamby Ambikairajah +2
Hand-crafted features, such as Mel-filterbanks, have traditionally been the choice for many audio processing applications. Recently, there has been a growing interest in learnable…
Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features
Hanyu Meng, Jeroen Breebaart, Jeremy Stoddard +2
Estimating frequency-varying acoustic parameters is essential for enhancing immersive perception in realistic spatial audio creation. In this paper, we propose a unified framework…
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…
Binaural Selective Attention Model for Target Speaker Extraction
Hanyu Meng, Qiquan Zhang, Xiangyu Zhang +2
The remarkable ability of humans to selectively focus on a target speaker in cocktail party scenarios is facilitated by binaural audio processing. In this paper, we present a binau…
An Exploration of Length Generalization in Transformer-Based Speech Enhancement
Qiquan Zhang, Hongxu Zhu, Xinyuan Qian +2
The use of Transformer architectures has facilitated remarkable progress in speech enhancement. Training Transformers using substantially long speech utterances is often infeasible…
Mamba in Speech: Towards an Alternative to Self-Attention
Xiangyu Zhang, Qiquan Zhang, Hexin Liu +6
Transformer and its derivatives have achieved success in diverse tasks across computer vision, natural language processing, and speech processing. To reduce the complexity of compu…
What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy Conditions
Hanyu Meng, Vidhyasaharan Sethu, Eliathamby Ambikairajah
There is increasing interest in the use of the LEArnable Front-end (LEAF) in a variety of speech processing systems. However, there is a dearth of analyses of what is actually lear…
An Empirical Study on the Impact of Positional Encoding in Transformer-based Monaural Speech Enhancement
Qiquan Zhang, Meng Ge, Hongxu Zhu +4
Transformer architecture has enabled recent progress in speech enhancement. Since Transformers are position-agostic, positional encoding is the de facto standard component used to…
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…
An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks
Zihan Pan, Yansong Chua, Jibin Wu +3
Auditory front-end is an integral part of a spiking neural network (SNN) when performing auditory cognitive tasks. It encodes the temporal dynamic stimulus, such as speech and audi…