Automatic speech recognition: a survey

Malik, Mishaim; Malik, Misbah; Mehmood, Khawar; Makhdoom, Imran

doi:10.1007/s11042-020-10073-7

Cited by 211 publications

(78 citation statements)

References 115 publications

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“…Therefore, it is evident that to tackle this issue we need to focus on the effective construction of the application domain of IoMT. The components of this application domain can be addressed as advanced level machine learning and deep learning [ 49 ], reasoning [ 50 ], natural language processing [ 51 ], speech recognition [ 52 ] and computer vision (image object recognition) [ 53 ], human-computer interaction, and dialog and narrative generation. From a global perspective, this can be used to incorporate the new generation hardware and software systems that imitate the human brain and cognitive functionality and thus enhance the human decision-making process.…”

Section: Discussionmentioning

confidence: 99%

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

Alam

Rahmani

2021

Sensors

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Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

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Section: Discussionmentioning

confidence: 99%

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

Alam

Rahmani

2021

Sensors

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“…Author details 1 College of Computer and Information Engineering, Hohai University, Nanjing, China. 2 School of Engineering Auditing, Jiangsu Key Laboratory of Public Project Audit, Nanjing Audit University, Nanjing, China.…”

Section: Abbreviationsmentioning

confidence: 99%

“…In the front-end processing, the Mel frequency cepstral coefficient (MFCC) is widely used to represent the speech signal [1]. Besides, the perceptual linear predictive (PLP) features [2], spectro-temporal features [3], and cochlear filter cepstral coefficients (CFCC) features [4] have also been successfully used for speech recognition. In the backend classification, the statistical acoustic models are commonly used, such as hidden Markov model (HMM) [5], *Correspondence: wupingping@nau.edu.cn 2 School of Engineering Auditing, Jiangsu Key Laboratory of Public Project Audit, Nanjing Audit University, Nanjing, China Full list of author information is available at the end of the article artificial neural network (ANN) [6], and dynamic Bayesian network (DBN) [7].…”

Section: Introductionmentioning

confidence: 99%

Feature compensation based on independent noise estimation for robust speech recognition

Lü

Lin

et al. 2021

J AUDIO SPEECH MUSIC PROC.

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In this paper, we propose a novel feature compensation algorithm based on independent noise estimation, which employs a Gaussian mixture model (GMM) with fewer Gaussian components to rapidly estimate the noise parameters from the noisy speech and monitor the noise variation. The estimated noise model is combined with a GMM with sufficient Gaussian mixtures to produce the noisy GMM for the clean speech estimation so that parameters are updated if and only if the noise variation occurs. Experimental results show that the proposed algorithm can achieve the recognition accuracy similar to that of the traditional GMM-based feature compensation, but significantly reduces the computational cost, and thereby is more useful for resource-limited mobile devices.

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“…D EEP Neural Networks (DNNs) have shown noticeable success in handling Computer Vision (CV) problems, such as image classification [1] , object detection [2], and face recognition [3]. They also have demonstrated great success in other complicated Machine Learning (ML) tasks such as speech recognition [4] and Natural Language Processing (NLP) [5], [6] . However, DNNs have not yet shown remark-able improvement in learning intrinsic concepts that lead to correct output labels [7].…”

Section: Introductionmentioning

confidence: 99%

Toward Improved Reliability of Deep Learning Based Systems Through Online Relabeling of Potential Adversarial Attacks

Al-Maliki¹,

Bouanani²,

Ahmad³

et al. 2022

Preprint

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<div>Deep Neural Networks (DDNs) have achieved tremendous success in handling various Machine Learning (ML) tasks, such as speech recognition, Natural Language Processing, and image classification. However, they have shown vulnerability to well-designed inputs called adversarial examples. Researchers in industry and academia have proposed many adversarial example defense techniques. However, none can provide complete robustness. The cutting-edge defense techniques offer partial reliability. Thus, complementing them with another layer of protection is a must, especially for mission-critical applications. This paper proposes a novel Online Selection and Relabeling Algorithm (OSRA) that opportunistically utilizes a limited number of crowdsourced workers (budget-constraint crowdsourcing) to maximize the ML system’s robustness. OSRA strives to use crowdsourced workers effectively by selecting the most suspicious inputs (the potential adversarial examples) and moving them to the crowdsourced workers to be validated and corrected (relabeled). As a result, the impact of adversarial examples gets reduced, and accordingly, the ML system becomes more robust. We also proposed a heuristic threshold selection method that contributes to enhancing the prediction system’s reliability. We empirically validated our proposed algorithm and found that it can efficiently and optimally utilize the allocated budget for crowdsourcing. It is also effectively integrated with a state-ofthe- art black-box (transfer-based) defense technique, resulting in a more robust system. Simulation results show that OSRA can outperform a random selection algorithm by 60% and achieve comparable performance to an optimal offline selection benchmark. They also show that OSRA’s performance has a positive correlation with system robustness.<br></div>

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Automatic speech recognition: a survey

Cited by 211 publications

References 115 publications

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

Feature compensation based on independent noise estimation for robust speech recognition

Toward Improved Reliability of Deep Learning Based Systems Through Online Relabeling of Potential Adversarial Attacks

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