32 citations · 64 across the 6 of their papers we have counts for
6 papers
Non-uniform Speaker Disentanglement For Depression Detection From Raw Speech Signals
Jinhan Wang, Vijay Ravi, Abeer Alwan
While speech-based depression detection methods that use speaker-identity features, such as speaker embeddings, are popular, they often compromise patient privacy. To address this…
Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASR
Ruchao Fan, Yunzheng Zhu, Jinhan Wang +1
Recently, self-supervised learning (SSL) from unlabelled speech data has gained increased attention in the automatic speech recognition (ASR) community. Typical SSL methods include…
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition
Ruchao Fan, Wei Chu, Peng Chang +1
Recently, end-to-end models have been widely used in automatic speech recognition (ASR) systems. Two of the most representative approaches are connectionist temporal classification…
Learning from human perception to improve automatic speaker verification in style-mismatched conditions
Amber Afshan, Abeer Alwan
Our prior experiments show that humans and machines seem to employ different approaches to speaker discrimination, especially in the presence of speaking style variability. The exp…
Attention-based conditioning methods using variable frame rate for style-robust speaker verification
Amber Afshan, Abeer Alwan
We propose an approach to extract speaker embeddings that are robust to speaking style variations in text-independent speaker verification. Typically, speaker embedding extraction…
Unsupervised Instance Discriminative Learning for Depression Detection from Speech Signals
Jinhan Wang, Vijay Ravi, Jonathan Flint +1
Major Depressive Disorder (MDD) is a severe illness that affects millions of people, and it is critical to diagnose this disorder as early as possible. Detecting depression from vo…