Youxiang Zhu

TL;DR: This paper determines the locations and lengths of inter-word pauses using the number of blank tokens from Wav2vec where the threshold for setting the pauses is automatically generated via BERT, enabling the fine-tuning of WavBERT with non-semantic information.

TL;DR: In this article, a variety of transfer learning models using commonly employed MobileNet (image), YAMNet (audio), Mockingjay (speech), and BERT (text) models were compared.

TL;DR: This paper explores the voice commands using a Voice-Assistant System (VAS), i.e., Amazon Alexa, from 40 older adults who were either Healthy Control (HC) participants or Mild Cognitive Impairment (MCI) participants, age 65 or older, to demonstrate the promise of future home-based cognitive assessments using Voice- Assistant Systems.

TL;DR: This paper exploits a Fully Convolutional Network to analyze the audio data of spontaneous speech for dementia detection, and builds a convolutional layer to produce a heatmap using Otsu's method for visualization, enabling to understand the impact of the time-series audio segments on the classification results.

TL;DR: In this paper, the authors used adversarial training to evaluate the impact of the perturbation in training samples on the detection model and found that some pauses are more sensitive to dementia than other pauses from the model's perspective, e.g. near to the verb "is".