Publications

Dual Script E2E Framework for Multilingual and Code-Switching ASR

Published in In the proceedings of Proc. Interspeech, 2021

In this paper, we use an in-house rule-based phoneme-level common label set (CLS) representation to train multilingual and code-switching ASR for Indian languages. We propose a modification to the E2E model, wherein the CLS representation and the native language characters are used simultaneously for training. We show our results on the multilingual and code-switching (MUCS) Read more

Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas

Published in PLOS Computational Biology, 2021

In contrast to the conventional input-output characterizations of neuronal responses to standard visual stimuli, here we asked whether six of the core visual areas have responses that are functionally distinct from each other for a given visual stimulus set, by applying machine learning techniques to distinguish the areas based on their activity patterns. Visual areas Read more

Evidence of Task-Independent Person-Specific Signatures in EEG using Subspace Techniques

Published in IEEE Transactions on Information Forensics and Security, 2021

The paper extends ideas from subspace-based text-independent speaker recognition and proposes novel modifications for modeling multi-channel EEG data. The proposed techniques assume that biometric information is present in the entire EEG signal and accumulate statistics across time in a high dimensional space. These high dimensional statistics are then projected to a lower dimensional space where Read more

Seizure Detection Using Time Delay Neural Networks and LSTMs

Published in In the proceedings of 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2020

In this paper, we propose a neural network system using the time-delay neural network to model temporal information (TDNN) and long short term memory (LSTM) layer to model spatial information. On the development subset of Temple University seizure dataset, the proposed system achieved a sensitivity of 23.32 % with 11.13 false alarms in 24 hours. Read more

Spoof Detection Using Time-Delay Shallow Neural Network and Feature Switching

Published in In the proceedings of 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2019

Inspired by the state-of-the-art x-vector based speaker verification approach, this paper proposes a time-delay shallow neural network (TD-SNN) for spoof detection for both logical and physical access. The novelty of the proposed TD-SNN system vis-a-vis conventional DNN systems is that it can handle variable length utterances during testing. Performance of the proposed TD-SNN systems and Read more

Time Warping Solutions for Classifying Artifacts in EEG

Published in In the proceedings of IEEE International Conference of Engineering in Medicine and Biology Society (EMBS), 2019

In this paper, we devise algorithms for detection and classification of artifacts. Classification of artifacts into head nod, jaw movement and eye-blink is performed using two different varieties of time warping; namely, linear time warping, and dynamic time warping. The average accuracy of 85% and 90% is obtained using the former, and the later, respectively. Read more

Subspace techniques for task independent EEG person identification

Published in In the proceedings of IEEE International Conference of Engineering in Medicine and Biology Society (EMBS), 2019

To effectively extract person-specific signatures present in EEG, it is necessary to define a subspace that enhances the biometric information and suppresses other nuisance factors. i-vector and x-vector are state-of-art subspace techniques used in speaker recognition. In this paper, novel modifications are proposed for both frameworks to project person-specific signatures from multi-channel EEG into a Read more

Spike Estimation from Fluorescence Signals Using High-Resolution Property of Group Delay

Published in IEEE Transactions on Signal Processing, 2019

While existing methods rely on data-driven methods and the physiology of neurons for modelling the spiking process, this work exploits the nature of the fluorescence responses to spikes using signal processing. We first motivate the problem by a novel analysis of the high-resolution property of minimum-phase group delay (GD) functions for multi-pole resonators. The resonators Read more