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Article ; Online: Reliability and validity of a widely-available AI tool for assessment of stress based on speech.

Yawer, Batul A / Liss, Julie / Berisha, Visar

2023 Volume 13, Issue 1, Page(s) 20224

Abstract: Cigna's online stress management toolkit includes an AI-based tool that purports to evaluate a person's psychological stress level based on analysis of their speech, the Cigna StressWaves Test (CSWT). In this study, we evaluate the claim that the CSWT is ...

Abstract	Cigna's online stress management toolkit includes an AI-based tool that purports to evaluate a person's psychological stress level based on analysis of their speech, the Cigna StressWaves Test (CSWT). In this study, we evaluate the claim that the CSWT is a "clinical grade" tool via an independent validation. The results suggest that the CSWT is not repeatable and has poor convergent validity; the public availability of the CSWT despite insufficient validation data highlights concerns regarding premature deployment of digital health tools for stress and anxiety management.
MeSH term(s)	Humans ; Speech ; Reproducibility of Results ; Artificial Intelligence
Language	English
Publishing date	2023-11-18
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-47153-1
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Article ; Online: Dysarthria detection based on a deep learning model with a clinically-interpretable layer.

Xu, Lingfeng / Liss, Julie / Berisha, Visar

JASA express letters

2023 Volume 3, Issue 1, Page(s) 15201

Abstract: Studies have shown deep neural networks (DNN) as a potential tool for classifying dysarthric speakers and controls. However, representations used to train DNNs are largely not clinically interpretable, which limits clinical value. Here, a model with a ... ...

Abstract	Studies have shown deep neural networks (DNN) as a potential tool for classifying dysarthric speakers and controls. However, representations used to train DNNs are largely not clinically interpretable, which limits clinical value. Here, a model with a bottleneck layer is trained to jointly learn a classification label and four clinically-interpretable features. Evaluation of two dysarthria subtypes shows that the proposed method can flexibly trade-off between improved classification accuracy and discovery of clinically-interpretable deficit patterns. The analysis using Shapley additive explanation shows the model learns a representation consistent with the disturbances that define the two dysarthria subtypes considered in this work.
MeSH term(s)	Humans ; Dysarthria/diagnosis ; Deep Learning ; Neural Networks, Computer
Language	English
Publishing date	2023-02-01
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	2691-1191
ISSN (online)	2691-1191
DOI	10.1121/10.0016833
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Article: Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection.

Zhang, Jianwei / Liss, Julie / Jayasuriya, Suren / Berisha, Visar

IEEE/ACM transactions on audio, speech, and language processing

2023 Volume 31, Page(s) 1348–1359

Abstract: Approximately 1.2% of the world's population has impaired voice production. As a result, automatic dysphonic voice detection has attracted considerable academic and clinical interest. However, existing methods for automated voice assessment often fail to ...

Abstract	Approximately 1.2% of the world's population has impaired voice production. As a result, automatic dysphonic voice detection has attracted considerable academic and clinical interest. However, existing methods for automated voice assessment often fail to generalize outside the training conditions or to other related applications. In this paper, we propose a deep learning framework for generating acoustic feature embeddings sensitive to vocal quality and robust across different corpora. A contrastive loss is combined with a classification loss to train our deep learning model jointly. Data warping methods are used on input voice samples to improve the robustness of our method. Empirical results demonstrate that our method not only achieves high in-corpus and cross-corpus classification accuracy but also generates good embeddings sensitive to voice quality and robust across different corpora. We also compare our results against three baseline methods on clean and three variations of deteriorated in-corpus and cross-corpus datasets and demonstrate that the proposed model consistently outperforms the baseline methods.
Language	English
Publishing date	2023-03-28
Publishing country	United States
Document type	Journal Article
ISSN	2329-9290
ISSN	2329-9290
DOI	10.1109/taslp.2023.3261753
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Article ; Online: TorchDIVA: An extensible computational model of speech production built on an open-source machine learning library.

Kinahan, Sean P / Liss, Julie M / Berisha, Visar

PloS one

2023 Volume 18, Issue 2, Page(s) e0281306

Abstract: The DIVA model is a computational model of speech motor control that combines a simulation of the brain regions responsible for speech production with a model of the human vocal tract. The model is currently implemented in Matlab Simulink; however, this ... ...

Abstract	The DIVA model is a computational model of speech motor control that combines a simulation of the brain regions responsible for speech production with a model of the human vocal tract. The model is currently implemented in Matlab Simulink; however, this is less than ideal as most of the development in speech technology research is done in Python. This means there is a wealth of machine learning tools which are freely available in the Python ecosystem that cannot be easily integrated with DIVA. We present TorchDIVA, a full rebuild of DIVA in Python using PyTorch tensors. DIVA source code was directly translated from Matlab to Python, and built-in Simulink signal blocks were implemented from scratch. After implementation, the accuracy of each module was evaluated via systematic block-by-block validation. The TorchDIVA model is shown to produce outputs that closely match those of the original DIVA model, with a negligible difference between the two. We additionally present an example of the extensibility of TorchDIVA as a research platform. Speech quality enhancement in TorchDIVA is achieved through an integration with an existing PyTorch generative vocoder called DiffWave. A modified DiffWave mel-spectrum upsampler was trained on human speech waveforms and conditioned on the TorchDIVA speech production. The results indicate improved speech quality metrics in the DiffWave-enhanced output as compared to the baseline. This enhancement would have been difficult or impossible to accomplish in the original Matlab implementation. This proof-of-concept demonstrates the value TorchDIVA can bring to the research community. Researchers can download the new implementation at: https://github.com/skinahan/DIVA_PyTorch.
MeSH term(s)	Humans ; Speech ; Ecosystem ; Software ; Computer Simulation ; Machine Learning
Language	English
Publishing date	2023-02-17
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0281306
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A speech-based prognostic model for dysarthria progression in ALS.

Stegmann, Gabriela / Charles, Sherman / Liss, Julie / Shefner, Jeremy / Rutkove, Seward / Berisha, Visar

Amyotrophic lateral sclerosis & frontotemporal degeneration

2023 , Page(s) 1–6

Abstract: ... ...

Abstract	Objective
Language	English
Publishing date	2023-06-12
Publishing country	England
Document type	Journal Article
ZDB-ID	2705049-X
ISSN	2167-9223 ; 2167-8421
ISSN (online)	2167-9223
ISSN	2167-8421
DOI	10.1080/21678421.2023.2222144
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Book ; Online: Does human speech follow Benford's Law?

Hsu, Leo / Berisha, Visar

2022

Abstract: Researchers have observed that the frequencies of leading digits in many man-made and naturally occurring datasets follow a logarithmic curve, with digits that start with the number 1 accounting for $\sim 30\%$ of all numbers in the dataset and digits ... ...

Abstract	Researchers have observed that the frequencies of leading digits in many man-made and naturally occurring datasets follow a logarithmic curve, with digits that start with the number 1 accounting for $\sim 30\%$ of all numbers in the dataset and digits that start with the number 9 accounting for $\sim 5\%$ of all numbers in the dataset. This phenomenon, known as Benford's Law, is highly repeatable and appears in lists of numbers from electricity bills, stock prices, tax returns, house prices, death rates, lengths of rivers, and naturally occurring images. In this paper we demonstrate that human speech spectra also follow Benford's Law on average. That is, when averaged over many speakers, the frequencies of leading digits in speech magnitude spectra follow this distribution, although with some variability at the individual sample level. We use this observation to motivate a new set of features that can be efficiently extracted from speech and demonstrate that these features can be used to classify between human speech and synthetic speech.
Keywords	Computer Science - Computation and Language ; Computer Science - Machine Learning
Publishing date	2022-03-24
Publishing country	us
Document type	Book ; Online
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Article ; Online: The value of brain MRI functional connectivity data in a machine learning classifier for distinguishing migraine from persistent post-traumatic headache.

Dumkrieger, Gina / Chong, Catherine D / Ross, Katherine / Berisha, Visar / Schwedt, Todd J

Frontiers in pain research (Lausanne, Switzerland)

2023 Volume 3, Page(s) 1012831

Abstract: Background: Post-traumatic headache (PTH) and migraine often have similar phenotypes. The objective of this exploratory study was to develop classification models to differentiate persistent PTH (PPTH) from migraine using clinical data and magnetic ... ...

Abstract	Background: Post-traumatic headache (PTH) and migraine often have similar phenotypes. The objective of this exploratory study was to develop classification models to differentiate persistent PTH (PPTH) from migraine using clinical data and magnetic resonance imaging (MRI) measures of brain structure and functional connectivity (fc). Methods: Thirty-four individuals with migraine and 48 individuals with PPTH attributed to mild TBI were included. All individuals completed questionnaires assessing headache characteristics, mood, sensory hypersensitivities, and cognitive function and underwent brain structural and functional imaging during the same study visit. Clinical features, structural and functional resting-state measures were included as potential variables. Classifiers using ridge logistic regression of principal components were fit on the data. Average accuracy was calculated using leave-one-out cross-validation. Models were fit with and without fc data. The importance of specific variables to the classifier were examined. Results: With internal variable selection and principal components creation the average accuracy was 72% with fc data and 63.4% without fc data. This classifier with fc data identified individuals with PPTH and individuals with migraine with equal accuracy. Conclusion: Multivariate models based on clinical characteristics, fc, and brain structural data accurately classify and differentiate PPTH vs. migraine suggesting differences in the neuromechanism and clinical features underlying both headache disorders.
Language	English
Publishing date	2023-01-09
Publishing country	Switzerland
Document type	Journal Article
ISSN	2673-561X
ISSN (online)	2673-561X
DOI	10.3389/fpain.2022.1012831
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Article: Visceral leishmaniasis in Kosovo: A case of misdiagnosis and diagnostic challenges.

Tolaj, Ilir / Mehmeti, Murat / Gashi, Hatixhe / Berisha, Fjorda / Gashi, Visar / Fejza, Hajrullah / Shala, Nexhmedin

IDCases

2023 Volume 32, Page(s) e01768

Abstract: Introduction: Visceral leishmaniasis (VL) is a parasitic disease caused by various Leishmania species and is a potentially life-threatening condition. The disease is highly endemic in several regions, including the Balkans, yet information regarding its ...

Abstract	Introduction: Visceral leishmaniasis (VL) is a parasitic disease caused by various Leishmania species and is a potentially life-threatening condition. The disease is highly endemic in several regions, including the Balkans, yet information regarding its prevalence in Kosovo is limited. Case presentation: In this case presentation, a 62-year-old man was admitted to a hospital in Kosovo due to a persistent high fever, and after extensive evaluations and treatments, he was diagnosed with fever of unknown origin (FUO) and transferred to a hospital in Turkey. An abscess of the psoas muscle caused by MRSA was found, however, pancytopenia persisted despite antibiotic treatment. Six months later, the patient was hospitalized again due to fever, chills, and night sweats. Microscopic examination and serological tests revealed the presence of Leishmania infantum in the bone marrow. Liposomal amphotericin B treatment resulted in a significant improvement in the patient's condition. Discussion: The diagnosis of VL can be challenging, and it can easily be misdiagnosed as other diseases, resulting in diagnostic delays and potentially fatal outcomes. In endemic regions such as the Balkans, it is crucial for physicians to be aware of this infection to avoid misdiagnosis or diagnostic delay. Early diagnosis and prompt treatment of VL are essential in preventing morbidity and mortality. Conclusion: This case highlights the significance of considering VL as a possible diagnosis in patients presenting with febrile illnesses accompanied by pancytopenia and splenomegaly, especially in endemic regions.
Language	English
Publishing date	2023-04-12
Publishing country	Netherlands
Document type	Case Reports
ZDB-ID	2745454-X
ISSN	2214-2509
ISSN	2214-2509
DOI	10.1016/j.idcr.2023.e01768
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Article ; Online: Speech Entrainment in Adolescent Conversations: A Developmental Perspective.

Wynn, Camille J / Barrett, Tyson S / Berisha, Visar / Liss, Julie M / Borrie, Stephanie A

Journal of speech, language, and hearing research : JSLHR

2023 Volume 66, Issue 8S, Page(s) 3132–3150

Abstract: Purpose: Defined as the similarity of speech behaviors between interlocutors, speech entrainment plays an important role in successful adult conversations. According to theoretical models of entrainment and research on motoric, cognitive, and social ... ...

Abstract	Purpose: Defined as the similarity of speech behaviors between interlocutors, speech entrainment plays an important role in successful adult conversations. According to theoretical models of entrainment and research on motoric, cognitive, and social developmental milestones, the ability to entrain should develop throughout adolescence. However, little is known about the specific developmental trajectory or the role of speech entrainment in conversational outcomes of this age group. The purpose of this study is to characterize speech entrainment patterns in the conversations of neurotypical early adolescents. Method: This study utilized a corpus of 96 task-based conversations between adolescents between the ages of 9 and 14 years and a comparison corpus of 32 task-based conversations between adults. For each conversational turn, two speech entrainment scores were calculated for 429 acoustic features across rhythmic, articulatory, and phonatory dimensions. Predictive modeling was used to evaluate the degree of entrainment and relationship between entrainment and two metrics of conversational success. Results: Speech entrainment increased throughout early adolescence but did not reach the level exhibited in conversations between adults. Additionally, speech entrainment was predictive of both conversational quality and conversational efficiency. Furthermore, models that included all acoustic features and both entrainment types performed better than models that only included individual acoustic feature sets or one type of entrainment. Conclusions: Our findings show that speech entrainment skills are largely developed during early adolescence with continued development possibly occurring across later adolescence. Additionally, results highlight the role of speech entrainment in successful conversation in this population, suggesting the import of continued exploration of this phenomenon in both neurotypical and neurodivergent adolescents. We also provide evidence of the value of using holistic measures that capture the multidimensionality of speech entrainment and provide a validated methodology for investigating entrainment across multiple acoustic features and entrainment types.
MeSH term(s)	Adult ; Humans ; Adolescent ; Child ; Speech ; Communication ; Phonation ; Speech Production Measurement ; Acoustics
Language	English
Publishing date	2023-04-18
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1364086-0
ISSN	1558-9102 ; 1092-4388
ISSN (online)	1558-9102
ISSN	1092-4388
DOI	10.1044/2023_JSLHR-22-00263
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Article ; Online: Wav2DDK: Analytical and Clinical Validation of an Automated Diadochokinetic Rate Estimation Algorithm on Remotely Collected Speech.

Kadambi, Prad / Stegmann, Gabriela M / Liss, Julie / Berisha, Visar / Hahn, Shira

Journal of speech, language, and hearing research : JSLHR

2023 Volume 66, Issue 8S, Page(s) 3166–3181

Abstract: Purpose: Oral diadochokinesis is a useful task in assessment of speech motor function in the context of neurological disease. Remote collection of speech tasks provides a convenient alternative to in-clinic visits, but scoring these assessments can be a ...

Abstract	Purpose: Oral diadochokinesis is a useful task in assessment of speech motor function in the context of neurological disease. Remote collection of speech tasks provides a convenient alternative to in-clinic visits, but scoring these assessments can be a laborious process for clinicians. This work describes Wav2DDK, an automated algorithm for estimating the diadochokinetic (DDK) rate on remotely collected audio from healthy participants and participants with amyotrophic lateral sclerosis (ALS). Method: Wav2DDK was developed using a corpus of 970 DDK assessments from healthy and ALS speakers where ground truth DDK rates were provided manually by trained annotators. The clinical utility of the algorithm was demonstrated on a corpus of 7,919 assessments collected longitudinally from 26 healthy controls and 82 ALS speakers. Corpora were collected via the participants' own mobile device, and instructions for speech elicitation were provided via a mobile app. DDK rate was estimated by parsing the character transcript from a deep neural network transformer acoustic model trained on healthy and ALS speech. Results: Algorithm estimated DDK rates are highly accurate, achieving .98 correlation with manual annotation, and an average error of only 0.071 syllables per second. The rate exactly matched ground truth for 83% of files and was within 0.5 syllables per second for 95% of files. Estimated rates achieve a high test-retest reliability ( Conclusion: We demonstrate a system for automated DDK estimation that increases efficiency of calculation beyond manual annotation. Thorough analytical and clinical validation demonstrates that the algorithm is not only highly accurate, but also provides a convenient, clinically relevant metric for tracking longitudinal decline in ALS, serving to promote participation and diversity of participants in clinical research. Supplemental material: https://doi.org/10.23641/asha.23787033.
MeSH term(s)	Humans ; Speech ; Amyotrophic Lateral Sclerosis ; Reproducibility of Results ; Speech Articulation Tests ; Algorithms
Language	English
Publishing date	2023-08-09
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1364086-0
ISSN	1558-9102 ; 1092-4388
ISSN (online)	1558-9102
ISSN	1092-4388
DOI	10.1044/2023_JSLHR-22-00282
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