Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Kadiri, Sudarsana"'
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 combining perceptual evaluation of speech with conventional acoustical
Externí odkaz:
http://arxiv.org/abs/2410.17028
Autor:
Ashvin, Aditya, Lahiri, Rimita, Kommineni, Aditya, Bishop, Somer, Lord, Catherine, Kadiri, Sudarsana Reddy, Narayanan, Shrikanth
The ability to reliably transcribe child-adult conversations in a clinical setting is valuable for diagnosis and understanding of numerous developmental disorders such as Autism Spectrum Disorder. Recent advances in deep learning architectures and av
Externí odkaz:
http://arxiv.org/abs/2409.16135
Autor:
Kommineni, Aditya, Bose, Digbalay, Feng, Tiantian, Kim, So Hyun, Tager-Flusberg, Helen, Bishop, Somer, Lord, Catherine, Kadiri, Sudarsana, Narayanan, Shrikanth
Clinical videos in the context of Autism Spectrum Disorder are often long-form interactions between children and caregivers/clinical professionals, encompassing complex verbal and non-verbal behaviors. Objective analyses of these videos could provide
Externí odkaz:
http://arxiv.org/abs/2409.13606
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 processing tools do not take speech ailments into account and are ther
Externí odkaz:
http://arxiv.org/abs/2407.11492
Autor:
Lee, Jihwan, Kommineni, Aditya, Feng, Tiantian, Avramidis, Kleanthis, Shi, Xuan, Kadiri, Sudarsana, Narayanan, Shrikanth
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 for Fully-End-to-end Speech Decoding from EEG signals. Our approach a
Externí odkaz:
http://arxiv.org/abs/2406.08644
Publikováno v:
in Proc. ICASSP, Rhodes Island, Greece, June 4-10, 2023
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 wav2vec 2.0 model is studied as a feature extractor to build detection
Externí odkaz:
http://arxiv.org/abs/2309.14107
Autor:
Kadiri, Sudarsana Reddy, Alku, Paavo
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing, Vol. 14, No. 2, pp. 367-379, February 2020
Automatic detection of voice pathology enables objective assessment and earlier intervention for the diagnosis. This study provides a systematic analysis of glottal source features and investigates their effectiveness in voice pathology detection. Gl
Externí odkaz:
http://arxiv.org/abs/2309.14080
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 28, pp. 1901-1914, 2020
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 formant tracking methods typically adopt a two-stage estimate-and-track
Externí odkaz:
http://arxiv.org/abs/2308.16540
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 manner by linear prediction (LP)-based methods. As LP-based formant esti
Externí odkaz:
http://arxiv.org/abs/2308.09051
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 features derived from the single frequency filtering (SFF) method: (1) SFF
Externí odkaz:
http://arxiv.org/abs/2308.09042