Perceptual Information Loss due to Impaired Speech Production

Asaei, Afsaneh; Cerňak, Miloš; Bourlard, Hervé

doi:10.1109/taslp.2017.2738445

Cited by 17 publications

(8 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Some Cases, Studies Incorporate More Than One Technique. Following That, the Same Study Was Replicated, Thereby Increasing the Overall Number of Research Studies Method/Technique % Reference MFCCs/derived features from MFCCs 35.4% [ 13 , 15 , 55 , 70 , 72 , 80 , 87 , 94 , 95 , 97–99 , 101 , 112 , 117 , 127 , 131 , 133 , 134 ] Spectro-Temporal of utterances/keywords 26.15% [ 12 , 14 , 15 , 26 , 63 , 72 , 87 , 89 , 90 , 97 , 101–103 , 110 , 112 , 124 , 136 ] Articulation way/Speech timing 18.5% [ 13 , 27 , 58 , 81 , 86–88 , 111 , 113 , 124 , 129 ] Glottal flow 7.7% [ 58 , …”

Section: Discussionmentioning

confidence: 99%

“…One significant gap lies in the limited availability of large, balanced, and high-quality annotated datasets for training and evaluating machine learning models for speech disorders. 7 , 11 , 12 , 102 , 105 , 117 , 120 , 123 , 138 The scarcity of such datasets limits the generalizability and validity of the results, leading to increased ambiguity and reduced statistical control. Moreover, it hinders the ability to draw strong inferences and develop and evaluate robust algorithms.…”

Section: Gaps and Future Directionsmentioning

confidence: 99%

“…Using one speech database may not represent the diversity of speech disorders and languages and may limit the generalizability and comparison of the results with other types of speech disorders languages. 117 , 120 , 134 , 138 Moreover, there is a lack of generalization of the machine learning models to other datasets or populations; 11 moreover, a lack of comparison between the proposed and existing models for performance, intelligibility, and speech capability loss assessment or accuracy rate prediction. 11 , 85 , 105 , 135 , 142 More practice-driven research is required to create standard and large datasets as a feasible approach for researchers to compare different methodologies and techniques, leading to improved results.…”

Section: Gaps and Future Directionsmentioning

confidence: 99%

See 2 more Smart Citations

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Brahmi,

Mahyoob,

Al-Sarem

et al. 2024

PRBM

View full text Add to dashboard Cite

Purpose Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies. Methods The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms. Originality This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014–2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse. Findings The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Gaps and Future Directionsmentioning

confidence: 99%

Section: Gaps and Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Brahmi,

Mahyoob,

Al-Sarem

et al. 2024

PRBM

View full text Add to dashboard Cite

show abstract

“…A RTIFICIAL neural networks are applied to a lot of fields, such as machine translation [1], speech recognition [2], object detection [3], robotics [4]- [6], intelligent control [7], etc. In neural networks, the gradient-descent-based algorithm is one of the most widely used optimization algorithms [8].…”

Section: Introductionmentioning

confidence: 99%

Activated Gradients for Deep Neural Networks

Liu¹,

Chen²,

Du³

et al. 2021

Preprint

View full text Add to dashboard Cite

Deep neural networks often suffer from poor performance or even training failure due to the ill-conditioned problem, the vanishing/exploding gradient problem, and the saddle point problem. In this paper, a novel method by acting the gradient activation function (GAF) on the gradient is proposed to handle these challenges. Intuitively, the GAF enlarges the tiny gradients and restricts the large gradient. Theoretically, this paper gives conditions that the GAF needs to meet, and on this basis, proves that the GAF alleviates the problems mentioned above. In addition, this paper proves that the convergence rate of SGD with the GAF is faster than that without the GAF under some assumptions. Furthermore, experiments on CIFAR, ImageNet, and PASCAL visual object classes confirm the GAF's effectiveness. The experimental results also demonstrate that the proposed method is able to be adopted in various deep neural networks to improve their performance. The source code is publicly available at https://github.com/LongJin-lab/Activated-Gradients-for-Deep-Neural-Networks.

show abstract

“…Their study focuses on the low-frequency region that is lower than 4000 Hz in the speech spectrum. However, the changes in the place of articulation and movement of articulators are closely related to high-frequency components of speech signals [27][28][29]. The distribution information in the high-frequency part of the speech signal is not considered in their research.…”

Section: Introductionmentioning

confidence: 99%

Acoustic analysis and detection of pharyngeal fricative in cleft palate speech using correlation of signals in independent frequency bands and octave spectrum prominent peak

Wang

Yin

et al. 2020

BioMed Eng OnLine

View full text Add to dashboard Cite

Background: Pharyngeal fricative is one typical compensatory articulation error of cleft palate speech. It passively influences daily communication for people who suffer from it. The automatic detection of pharyngeal fricatives in cleft palate speech can provide information for clinical doctors and speech-language pathologists to aid in diagnosis. Results: This paper proposes two features (CSIFs: correlation of signals in independent frequency bands; OSPP: octave spectrum prominent peak) to detect pharyngeal fricative speech. CSIFs feature is proposed to detect the distribution characteristics of frequency components in pharyngeal fricative speech caused by the changed place of articulation and movement of articulators. While OSPP is presented to reflect the concentration degree of prominent peak which is closely related to the place of articulation in pharyngeal fricative, both features are investigated to relate to the altered production process of pharyngeal fricative. To evaluate the capability of these two features to detect pharyngeal fricative, we collected a speech database covering all the types of initial consonants in which pharyngeal fricatives occur. In this detection task, the classifier used to discriminate pharyngeal fricative speech and normal speech is based on ensemble learning. Conclusion: The detection accuracy obtained with CSIFs and OSPP features ranges from 83.5 to 84.5% and from 85 to 87%, respectively. When these two features are combined, the detection accuracy for pharyngeal fricative speech ranges from 88 to 89%, with an AUC (area under the receiver operating characteristic curve) value of 93%.

show abstract

Perceptual Information Loss due to Impaired Speech Production

Cited by 17 publications

References 37 publications

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Activated Gradients for Deep Neural Networks

Acoustic analysis and detection of pharyngeal fricative in cleft palate speech using correlation of signals in independent frequency bands and octave spectrum prominent peak

Contact Info

Product

Resources

About