5 accepted papers
Automatic Speech Recognition (ASR) holds immense potential to provide an effective interface for assistive technologies, but its performance remains unsatisfactory for people with speech impairments such as dysarthria. Existing ASR systems struggle to accurately recognize dysarthric speech due to th…
In this paper, we present a speaker-independent dysarthric speech recognition system, with a focus on evaluating the recently released Speech Accessibility Project (SAP-1005) dataset, which includes speech data from individuals with Parkinson’s disease (PD). Despite the growing body of research in d…
We propose a new meta learning based framework for low resource speech recognition that improves the previous model agnostic meta learning (MAML) approach. The MAML is a simple yet powerful meta learning approach. However, the MAML presents some core deficiencies such as training instabilities and s…
We propose a novel pitch estimation technique called DeepF0, which leverages the available annotated data to directly learns from the raw audio in a data-driven manner. f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> estimation is important in…
In this letter, we present the design and development of a new robotic system for MRI-guided breast biopsy. The robot's structure is nonmagnetic, therefore, it can safely operate from within the scanner's magnetic bore; it has a compact Cartesian mechanism that allows it perform needle insertion tas