113 citations · 219 across the 26 of their papers we have counts for
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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…
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…
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…
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…
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…
Severity Classification of Parkinson's Disease from Speech using Single Frequency Filtering-based Features
Sudarsana Reddy Kadiri, Manila Kodali, Paavo Alku
Developing objective methods for assessing the severity of Parkinson's disease (PD) is crucial for improving the diagnosis and treatment. This study proposes two sets of novel feat…
Investigation of Self-supervised Pre-trained Models for Classification of Voice Quality from Speech and Neck Surface Accelerometer Signals
Sudarsana Reddy Kadiri, Farhad Javanmardi, Paavo Alku
Prior studies in the automatic classification of voice quality have mainly studied the use of the acoustic speech signal as input. Recently, a few studies have been carried out by…
End-to-end Ensemble-based Feature Selection for Paralinguistics Tasks
Tamás Grósz, Mittul Singh, Sudarsana Reddy Kadiri +2
The events of recent years have highlighted the importance of telemedicine solutions which could potentially allow remote treatment and diagnosis. Relatedly, Computational Paraling…
Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
Dhananjaya Gowda, Bajibabu Bollepalli, Sudarsana Reddy Kadiri +1
Formant tracking is investigated in this study by using trackers based on dynamic programming (DP) and deep neural nets (DNNs). Using the DP approach, six formant estimation method…
Aalto's End-to-End DNN systems for the INTERSPEECH 2020 Computational Paralinguistics Challenge
Tamás Grósz, Mittul Singh, Sudarsana Reddy Kadiri +2
End-to-end neural network models (E2E) have shown significant performance benefits on different INTERSPEECH ComParE tasks. Prior work has applied either a single instance of an E2E…