113 citations · 219 across the 26 of their papers we have counts for
29 papers
Layer-wise Cross-Lingual Depression Detection from Speech: Analysis with Contrastive Alignment
Anisha Pattanayak, Hanie Kang, Huang-Cheng Chou +2
Significant disparities exist in the diagnosis and clinical presentation of depression across different linguistic populations. Speech-based depression detection performs well mono…
Autoencoder based optimized SSL representations: Complexity Minimization and improved Dysarthric ASR
Paban Sapkota, Hemant Kumar Kathania, Mikko Kurimo +2
Self-supervised learning (SSL) models extract rich speech representations but often come with high-dimensional features, increasing computational complexity. This work explores an…
Improving End-to-End Speech Recognition for Dysarthric Speech through In-Domain Data Augmentation
Paban Sapkota, Hemant Kumar Kathania, Sudarsana Reddy Kadiri +1
Dysarthric speech recognition is crucial for facilitating effective communication among individuals with dysarthria. However, accurately recognizing dysarthric speech poses signifi…
Systematic Study of Dysarthric Speech Recognition: Spectral Features and Acoustic Models
Paban Sapkota, Hemant Kumar Kathania, Mikko Kurimo +2
The challenge associated with recognizing dysarthric speech primarily arises from pronounced acoustic variability attributed to impaired articulatory precision. Past research has d…
Single frequency filtering based multi-speaker direction of arrival estimation from stereo recordings
Sushmita Thakallapalli, Sudarsana Reddy Kadiri, Nilesh Madhu +1
Robust direction-of-arrival (DoA) estimation from noisy and reverberant microphone signals remains challenging. Conventional estimators such as generalized cross-correlation (GCC)…
An Approach to Simultaneous Acquisition of Real-Time MRI Video, EEG, and Surface EMG for Articulatory, Brain, and Muscle Activity During Speech Production
Jihwan Lee, Parsa Razmara, Kevin Huang +16
Speech production is a complex process spanning neural planning, motor control, muscle activation, and articulatory kinematics. While the acoustic speech signal is the most accessi…
voice2mode: Phonation Mode Classification in Singing using Self-Supervised Speech Models
Aju Ani Justus, Ruchit Agrawal, Sudarsana Reddy Kadiri +1
We present voice2mode, a method for classification of four singing phonation modes (breathy, neutral (modal), flow, and pressed) using embeddings extracted from large self-supervis…
Zero-Shot KWS for Children's Speech using Layer-Wise Features from SSL Models
Subham Kutum, Abhijit Sinha, Hemant Kumar Kathania +2
Numerous methods have been proposed to enhance Keyword Spotting (KWS) in adult speech, but children's speech presents unique challenges for KWS systems due to its distinct acoustic…
Can Layer-wise SSL Features Improve Zero-Shot ASR Performance for Children's Speech?
Abhijit Sinha, Hemant Kumar Kathania, Sudarsana Reddy Kadiri +1
Automatic Speech Recognition (ASR) systems often struggle to accurately process children's speech due to its distinct and highly variable acoustic and linguistic characteristics. W…
Layer-Wise Analysis of Self-Supervised Representations for Age and Gender Classification in Children's Speech
Abhijit Sinha, Harishankar Kumar, Mohit Joshi +3
Children's speech presents challenges for age and gender classification due to high variability in pitch, articulation, and developmental traits. While self-supervised learning (SS…
Time-Resolved EEG Decoding of Semantic Processing Reveals Altered Neural Dynamics in Depression and Suicidality
Woojae Jeong, Aditya Kommineni, Kleanthis Avramidis +15
Depression and suicidality affect cognitive and emotional processes, yet objective, task-evoked neural readouts of mental health remain limited. We investigated the spatiotemporal…
Neural Responses to Affective Sentences Reveal Signatures of Depression
Aditya Kommineni, Woojae Jeong, Kleanthis Avramidis +13
Major Depressive Disorder (MDD) is a highly prevalent mental health condition, and a deeper understanding of its neurocognitive foundations is essential for identifying how core fu…
Towards disentangling the contributions of articulation and acoustics in multimodal phoneme recognition
Sean Foley, Hong Nguyen, Jihwan Lee +4
Although many previous studies have carried out multimodal learning with real-time MRI data that captures the audio-visual kinematics of the vocal tract during speech, these studie…
Deep Learning Characterizes Depression and Suicidal Ideation from Eye Movements
Kleanthis Avramidis, Woojae Jeong, Aditya Kommineni +14
Identifying physiological and behavioral markers for mental health conditions is a longstanding challenge in psychiatry. Depression and suicidal ideation, in particular, lack objec…
Enhancing Listened Speech Decoding from EEG via Parallel Phoneme Sequence Prediction
Jihwan Lee, Tiantian Feng, Aditya Kommineni +2
Brain-computer interfaces (BCI) offer numerous human-centered application possibilities, particularly affecting people with neurological disorders. Text or speech decoding from bra…
Can a Machine Distinguish High and Low Amount of Social Creak in Speech?
Anne-Maria Laukkanen, Sudarsana Reddy Kadiri, Shrikanth Narayanan +1
Objectives: ncreased prevalence of social creak particularly among female speakers has been reported in several studies. The study of social creak has been previously conducted by…
Towards Child-Inclusive Clinical Video Understanding for Autism Spectrum Disorder
Aditya Kommineni, Digbalay Bose, Tiantian Feng +6
Clinical videos in the context of Autism Spectrum Disorder are often long-form interactions between children and caregivers/clinical professionals, encompassing complex verbal and…
Evaluation of Speech Foundation Models for ASR on Child-Adult Conversations in Autism Diagnostic Sessions
Aditya Ashvin, Rimita Lahiri, Aditya Kommineni +4
Reliable transcription of child-adult conversations in clinical settings is crucial for diagnosing developmental disorders like Autism. Recent advances in deep learning and availab…
MMSD-Net: Towards Multi-modal Stuttering Detection
Liangyu Nie, Sudarsana Reddy Kadiri, Ruchit Agrawal
Stuttering is a common speech impediment that is caused by irregular disruptions in speech production, affecting over 70 million people across the world. Standard automatic speech…
Toward Fully-End-to-End Listened Speech Decoding from EEG Signals
Jihwan Lee, Aditya Kommineni, Tiantian Feng +4
Speech decoding from EEG signals is a challenging task, where brain activity is modeled to estimate salient characteristics of acoustic stimuli. We propose FESDE, a novel framework…
Wav2vec-based Detection and Severity Level Classification of Dysarthria from Speech
Farhad Javanmardi, Saska Tirronen, Manila Kodali +2
Automatic detection and severity level classification of dysarthria directly from acoustic speech signals can be used as a tool in medical diagnosis. In this work, the pre-trained…
Analysis and Detection of Pathological Voice using Glottal Source Features
Sudarsana Reddy Kadiri, Paavo Alku
Automatic detection of voice pathology enables objective assessment and earlier intervention for the diagnosis. This study provides a systematic analysis of glottal source features…
Time-Varying Quasi-Closed-Phase Analysis for Accurate Formant Tracking in Speech Signals
Dhananjaya Gowda, Sudarsana Reddy Kadiri, Brad Story +1
In this paper, we propose a new method for the accurate estimation and tracking of formants in speech signals using time-varying quasi-closed-phase (TVQCP) analysis. Conventional f…
Refining a Deep Learning-based Formant Tracker using Linear Prediction Methods
Paavo Alku, Sudarsana Reddy Kadiri, Dhananjaya Gowda
In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven ma…