25 citations · 37 across the 8 of their papers we have counts for
8 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…
A Step Towards Preserving Speakers' Identity While Detecting Depression Via Speaker Disentanglement
Vijay Ravi, Jinhan Wang, Jonathan Flint +1
Preserving a patient's identity is a challenge for automatic, speech-based diagnosis of mental health disorders. In this paper, we address this issue by proposing adversarial disen…
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…
Automatic Dialect Density Estimation for African American English
Alexander Johnson, Kevin Everson, Vijay Ravi +3
In this paper, we explore automatic prediction of dialect density of the African American English (AAE) dialect, where dialect density is defined as the percentage of words in an u…
FrAUG: A Frame Rate Based Data Augmentation Method for Depression Detection from Speech Signals
Vijay Ravi, Jinhan Wang, Jonathan Flint +1
In this paper, a data augmentation method is proposed for depression detection from speech signals. Samples for data augmentation were created by changing the frame-width and the f…
Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion
Vijay Ravi, Yile Gu, Ankur Gandhe +5
End-to-end automatic speech recognition (ASR) systems, such as recurrent neural network transducer (RNN-T), have become popular, but rare word remains a challenge. In this paper, w…
Variable frame rate-based data augmentation to handle speaking-style variability for automatic speaker verification
Amber Afshan, Jinxi Guo, Soo Jin Park +3
The effects of speaking-style variability on automatic speaker verification were investigated using the UCLA Speaker Variability database which comprises multiple speaking styles p…
Exploring the Use of an Unsupervised Autoregressive Model as a Shared Encoder for Text-Dependent Speaker Verification
Vijay Ravi, Ruchao Fan, Amber Afshan +2
In this paper, we propose a novel way of addressing text-dependent automatic speaker verification (TD-ASV) by using a shared-encoder with task-specific decoders. An autoregressive…