Classification of Huntington Disease Using Acoustic and Lexical Features

Perez, Matthew; Jin, Wenyu; Le, Duc; Carlozzi, Noelle E.; Dayalu, Praveen; Roberts, Angela; Provost, Emily Mower

doi:10.21437/interspeech.2018-2029

Cited by 34 publications

(29 citation statements)

References 25 publications

(29 reference statements)

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“…A multi-task learning technique to jointly solve dysarthria detection and speech reconstruction tasks was explored by encoding dysarthric speech to a lower dimensional latent space in [34]. Speech rate, pauses, fillers, and Goodness of Pronunciation (GoP) were used as discriminating features to differentiate healthy controls (HC) from individuals with Huntington disease using Long Short Term Memory -Recurrent Neural Network (LSTM-RNN) and Deep Neural Network (DNN) [35]. Classification of patients with Amyotrophic Lateral Sclerosis (ALS), Parkinsons Disease (PD), and Healthy Control (HC) using a Convolutional Neural Network -Long Short Term Memory (CNN-LSTM) based transfer learning framework was proposed in [36].…”

Section: Reference-free Approachesmentioning

confidence: 99%

Automatic speaker independent dysarthric speech intelligibility assessment system

Tripathi

Bhosale

Kopparapu

2021

Computer Speech & Language

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Dysarthria is a condition which hampers the ability of an individual to control the muscles that play a major role in speech delivery. The loss of fine control over muscles that assist the movement of lips, vocal chords, tongue and diaphragm results in abnormal speech delivery. One can assess the severity level of dysarthria by analyzing the intelligibility of speech spoken by an individual. Continuous intelligibility assessment helps speech language pathologists not only study the impact of medication but also allows them to plan personalized therapy. It helps the clinicians immensely if the intelligibility assessment system is reliable, automatic, simple for (a) patients to undergo and (b) clinicians to interpret. Lack of availability of dysarthric data has resulted in development of speaker dependent automatic intelligibility assessment systems which requires patients to speak a large number of utterances. In this paper, we propose (a) a cost minimization procedure to select an optimal (small) number of utterances that need to be spoken by the dysarthric patient, (b) four different speaker independent intelligibility assessment systems which require the patient to speak a small number of words, and (c) the assessment score is close to the perceptual score that the Speech Language Pathologist (SLP) can relate to. The need for small number of utterances to be spoken by the patient and the score being relatable to the SLP benefits both the dysarthric patient and the clinician from usability perspective.

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Section: Reference-free Approachesmentioning

confidence: 99%

Automatic speaker independent dysarthric speech intelligibility assessment system

Tripathi

Bhosale

Kopparapu

2021

Computer Speech & Language

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“…But also conditions beyond the speech production can be detected from voice samples, including Huntington's disease [76], Parkinson's disease [19], amyotrophic lateral sclerosis [74], asthma [104], Alzheimer's disease [27], and respiratory tract infections caused by the common cold and flu [20]. The sound of a person's voice may even serve as an indicator of overall fitness and long-term health [78,103].…”

Section: Speaker Pathologymentioning

confidence: 99%

Privacy Implications of Voice and Speech Analysis – Information Disclosure by Inference

Kröger

Lutz

Raschke

2020

Privacy and Identity Management. Data for Better Living: AI and Privacy

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Internet-connected devices, such as smartphones, smartwatches, and laptops, have become ubiquitous in modern life, reaching ever deeper into our private spheres. Among the sensors most commonly found in such devices are microphones. While various privacy concerns related to microphone-equipped devices have been raised and thoroughly discussed, the threat of unexpected inferences from audio data remains largely overlooked. Drawing from literature of diverse disciplines, this paper presents an overview of sensitive pieces of information that can, with the help of advanced data analysis methods, be derived from human speech and other acoustic elements in recorded audio. In addition to the linguistic content of speech, a speaker's voice characteristics and manner of expression may implicitly contain a rich array of personal information, including cues to a speaker's biometric identity, personality, physical traits, geographical origin, emotions, level of intoxication and sleepiness, age, gender, and health condition. Even a person's socioeconomic status can be reflected in certain speech patterns. The findings compiled in this paper demonstrate that recent advances in voice and speech processing induce a new generation of privacy threats.

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“…Random Forest (RF) and SVM methods were used for the evaluation of detection accuracy (best achieved result is 96.37% with RF classifier). Perez et al [41] differentiate between healthy controls and HD patients) based on acoustic and lexical features (MFCC, GMM, pause, speech rate, goodness of Pronunciation (GoP) [42]). The results were evaluated with k-Nearest Neighbours (k-NN) and Long-Short-Term Memory Recurrent Neural Networks (LSTM-RNN) algorithms (0.87 correlation).…”

Section: Related Workmentioning

confidence: 99%

Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features

et al. 2020

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We adopt Bidirectional Long Short-Term Memory (BiLSTM) neural network and Wavelet Scattering Transform with Support Vector Machine (WST-SVM) classifier for detecting speech impairments of patients at the early stage of central nervous system disorders (CNSD). The study includes 339 voice samples collected from 15 subjects: 7 patients with early stage CNSD (3 Huntington, 1 Parkinson, 1 cerebral palsy, 1 post stroke, 1 early dementia), other 8 subjects were healthy. Speech data is collected using voice recorder from Neural Impairment Test Suite (NITS) mobile app. Features are extracted from pitch contours, Mel-frequency cepstral coefficients (MFCC), Gammatone cepstral coefficients (GTCC), Gabor (analytic Morlet) wavelet and auditory spectrograms. 94.50% (BiLSTM) and 96.3% (WST-SVM) accuracy is achieved for solving healthy vs. impaired classification problem. The developed method can be applied for automated CNSD patient health state monitoring and clinical decision support systems as well as a part of Internet of Medical Things (IoMT). INDEX TERMS Neural impairment, mobile app, deep learning, wavelet scattering, decision support, speech processing, digital health, Internet of Medical Things.

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Classification of Huntington Disease Using Acoustic and Lexical Features

Cited by 34 publications

References 25 publications

Automatic speaker independent dysarthric speech intelligibility assessment system

Automatic speaker independent dysarthric speech intelligibility assessment system

Privacy Implications of Voice and Speech Analysis – Information Disclosure by Inference

Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features

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