1 citations · 2 across the 5 of their papers we have counts for
7 papers
Ultra2Speech -- A Deep Learning Framework for Formant Frequency Estimation and Tracking from Ultrasound Tongue Images
Pramit Saha, Yadong Liu, Bryan Gick +1
Thousands of individuals need surgical removal of their larynx due to critical diseases every year and therefore, require an alternative form of communication to articulate speech…
Learning Joint Articulatory-Acoustic Representations with Normalizing Flows
Pramit Saha, Sidney Fels
The articulatory geometric configurations of the vocal tract and the acoustic properties of the resultant speech sound are considered to have a strong causal relationship. This pap…
EEG-to-F0: Establishing artificial neuro-muscular pathway for kinematics-based fundamental frequency control
Himanshu Goyal, Pramit Saha, Bryan Gick +1
The fundamental frequency (F0) of human voice is generally controlled by changing the vocal fold parameters (including tension, length and mass), which in turn is manipulated by th…
SPEAK YOUR MIND! Towards Imagined Speech Recognition With Hierarchical Deep Learning
Pramit Saha, Muhammad Abdul-Mageed, Sidney Fels
Speech-related Brain Computer Interface (BCI) technologies provide effective vocal communication strategies for controlling devices through speech commands interpreted from brain s…
Deep Learning the EEG Manifold for Phonological Categorization from Active Thoughts
Pramit Saha, Muhammad Abdul-Mageed, Sidney Fels
Speech-related Brain Computer Interfaces (BCI) aim primarily at finding an alternative vocal communication pathway for people with speaking disabilities. As a step towards full dec…
Hierarchical Deep Feature Learning For Decoding Imagined Speech From EEG
Pramit Saha, Sidney Fels
We propose a mixed deep neural network strategy, incorporating parallel combination of Convolutional (CNN) and Recurrent Neural Networks (RNN), cascaded with deep autoencoders and…