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Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
[article]
2021
arXiv
pre-print
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Here, we develop and compare different deep learning techniques to classify the intelligibility of disordered speech on selected phrases. ...
Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of speech impairment. ...
We thank Katie Seaver for assessing and labeling a portion of the speech samples, Aren Jansen for advice on the CNN-ResNetish model, Shanqing Cai and Dick Lyon for reviews on a draft of this work, and ...
arXiv:2107.03985v1
fatcat:kad3cznlj5b75lulhhmzamvrze
Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition
[article]
2022
arXiv
pre-print
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The current study explores the usefulness of using Wav2Vec self-supervised speech representations as features for training an ASR system for dysarthric speech. ...
Compared to using Fbank features, XLSR-based features reduced WERs by 6.8%, 22.0%, and 7.0% for the UASpeech, PC-GITA, and EasyCall corpus, respectively. ...
Speech representation learning models such as Wav2Vec2.0 [2] , and Hubert [3] , have shown that learned representations produce state-of-the-art results on a variety of speech tasks: Speaker and language ...
arXiv:2204.01670v1
fatcat:prnagcdntvcwnmvsvvdskgve4i
A Robust Isolated Automatic Speech Recognition System using Machine Learning Techniques
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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The basic stages of speech recognition system are pre-processing, feature extraction and feature selection and classification. ...
For example speech recognition, speaker verification and speaker recognition. ...
There are four basic methods of learning on the basis of machine gaining knowledge to respond correctly are: Supervised learning Un-supervised learning Semi-supervised learning Active learning ...
doi:10.35940/ijitee.j8765.0881019
fatcat:n7vfdkeehfavzf2utjlpuggiui
An Extensive Review of Feature Extraction Techniques, Challenges and Trends in Automatic Speech Recognition
2019
International Journal of Image Graphics and Signal Processing
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In order to recognize the areas of further research in ASR, one must be aware of the current approaches, challenges faced by each and issues that needs to be addressed. ...
Therefore, in this paper human speech production mechanism is discussed. The various speech recognition techniques and models are addressed in detail. ...
In
binary SVM, features are
classified into two classes,
each class for recognized and
unrecognized speaker.
Supervised Learning
Method
Simple operation. ...
doi:10.5815/ijigsp.2019.05.01
fatcat:3uidt4wvofffvmuqlnanaegzjq
Simulating dysarthric speech for training data augmentation in clinical speech applications
[article]
2018
arXiv
pre-print
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Training machine learning algorithms for speech applications requires large, labeled training data sets. ...
We evaluate the efficacy of our approach using both objective and subjective criteria. ...
Due to a lack of data, machine learning models used in the study of pathological speech are typically limited to simple unsupervised metrics [13] , or flat supervised models [14] [15] . ...
arXiv:1804.10325v1
fatcat:txbs6yfjpfegpddbl6qo7udiym
Efficient Collection and Representation of Preverbal Data in Typical and Atypical Development
2020
Journal of nonverbal behavior
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In this paper, we give a methodological overview of current strategies for collecting and acoustically representing preverbal data for intelligent audio analysis paradigms. ...
Efficiency in the context of data collection and data representation is discussed. ...
In contrast, dynamic modeling was investigated by applying a neural network classifier on the basis of the LLDs of the ComParE set. ...
doi:10.1007/s10919-020-00332-4
pmid:33088008
pmcid:PMC7561537
fatcat:goinklywmnaljpgnaqd5rbs3tq
2020 Index IEEE Transactions on Affective Computing Vol. 11
2021
IEEE Transactions on Affective Computing
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The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, T-AFFC July-Emotion Recognition on Twitter: Comparative Study and Training a Unison Model. ...
doi:10.1109/taffc.2021.3055662
fatcat:het65admgnbbvn4fdzdgmftuqu
A Review of Automated Speech and Language Features for Assessment of Cognition and Thought Disorders
2019
IEEE Journal on Selected Topics in Signal Processing
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2019 pre-print arXiv:1906.01157v1 fatcat:hz6hv5pm4fhg5kgtwddfompjjuOther Versions
This work relies on extracting a set of features from recorded and transcribed speech for objective assessments of speech and language, early diagnosis of neurological disease, and tracking of disease ...
With an emphasis on cognitive and thought disorders, in this paper we provide a review of existing speech and language features used in this domain, discuss their clinical application, and highlight their ...
For classifying clinical (patients with schizophrenia and bipolar I disorder) subjects and healthy control subjects, the selected feature subset achieved receiver operating characteristic (ROC) area under ...
doi:10.1109/jstsp.2019.2952087
pmid:33907590
pmcid:PMC8074691
fatcat:a6t24cpp6jbdxbxq5wzd3uz6jq
Us and them: identifying cyber hate on Twitter across multiple protected characteristics
2016
EPJ Data Science
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To support the automatic detection of cyber hate online, specifically on Twitter, we build multiple individual models to classify cyber hate for a range of protected characteristics including race, disability ...
for different types of cyber hate beyond the use of a Bag of Words and known hateful terms. ...
supervised machine classifiers, and is based on human agreement on which class a piece of text belongs to. ...
doi:10.1140/epjds/s13688-016-0072-6
pmid:32355598
pmcid:PMC7175598
fatcat:r55bks5wazhw3ligikn5d2ptze
Classification of Speech Dysfluencies Using Speech Parameterization Techniques and Multiclass SVM
[chapter]
2013
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Stuttering is a fluency disorder characterized by the occurrences of dysfluencies in normal flow of speech, such as repetitions, prolongations and interjection and so on. ...
It is one of the serious problems in speech pathology. ...
It is supervised learning technique that uses a labeled data set for training and tries to find a decision function that classifies best the training data. ...
doi:10.1007/978-3-642-37949-9_26
fatcat:v2mpro4r3nbkdpe2oir4cu2vyy
Fast screening for children's developmental language disorders via comprehensive speech ability evaluation—using a novel deep learning framework
2020
Annals of Translational Medicine
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Developmental language disorders (DLDs) are the most common developmental disorders in children. For screening DLDs, speech ability (SA) is one of the most important indicators. ...
In this paper, we propose a solution for the fast screening of children's DLDs based on a comprehensive SA evaluation and a deep framework of machine learning. ...
a rough SA supervision for our deep model. ...
doi:10.21037/atm-19-3097
pmid:32617327
pmcid:PMC7327328
fatcat:55yavenqejee5dciq5tiz3d3zi
An Information Retrieval Approach to Building Datasets for Hate Speech Detection
[article]
2021
arXiv
pre-print
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To intelligently and efficiently select which tweets to annotate, we apply standard IR techniques of pooling and active learning. ...
Annotator rationales we collect not only justify labeling decisions but also enable future work opportunities for dual-supervision and/or explanation generation in modeling. ...
We thank the many talented Amazon Mechanical Turk workers who contributed to our study and made it possible. ...
arXiv:2106.09775v3
fatcat:56cg2t7nwbfe3lwdqw7z2eqjoy
Survey on Deep Neural Networks in Speech and Vision Systems
[article]
2019
arXiv
pre-print
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This survey begins by providing background and evolution of some of the most successful deep learning models for intelligent vision and speech systems to date. ...
To our knowledge, this paper provides one of the most comprehensive surveys on the latest developments in intelligent vision and speech applications from the perspectives of both software and hardware ...
ACKNOWLEDGMENT The authors would like to acknowledge partial funding of this work by the National Science Foundation (NSF) through a grant (Award# ECCS 1310353) and the National Institute of Health (NIH ...
arXiv:1908.07656v2
fatcat:7acubicqzzac3dqemkiccoogm4
The Use of Machine Learning Algorithms in the Classification of Sound
2022
International Journal of Service Science Management Engineering and Technology
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With regard to Ecoacoustics, studies on extreme events such as tornadoes and earthquakes for early detection and warning systems were lacking. ...
This study is a systematic review of literature on the classification of sounds in three domains - Bioacoustics, Biomedical acoustics, and Ecoacoustics. ...
Additionally, a semi-supervised learning technique called active learning was used to minimize the demand for human descriptions on sound classification training models .
Figure 6. ...
doi:10.4018/ijssmet.298667
fatcat:gygkfeoblrfihjzxlm46a7labe
Modeling the Progression of Speech Deficits in Cerebellar Ataxia using a Mixture Mixed-effect Machine Learning Framework
2021
IEEE Access
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ACKNOWLEDGMENT The authors would like to thank the Royal Victorian Eye and Ear Hospital (RVEEH), the Florey Institute of Neuroscience and Mental Health, Melbourne, Australia and CSIRO Data61 for their ...
: hereafter 'RT')). 2) Speech Task 2: Utter the phrase British Constitution (BC: a classical phrase for eliciting the features of ataxic speech) thrice. ...
exploitation of the selected features' change over two timepoints. 3) Classify the subjects into two groups based on the mixture extensions of the multivariate model. ...
doi:10.1109/access.2021.3114328
fatcat:vs2iawvdwvbg3gf7xsfg6gbfly
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