State of the art in statistical methods for language and speech processing

Bellegarda, Jerome R.; Monz, Christof

doi:10.1016/j.csl.2015.07.001

Cited by 33 publications

(14 citation statements)

References 76 publications

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“…Virtual agents roots are traced to legacy systems which used speech recognition to offer ground-breaking technologies for their time. VAL (Voice Activated Link) and Sphinx-II are such examples of systems that were ahead of their era and were later replaced by more advanced and voice-enabled with dialogue capabilities solutions [4]. Conversational agent's audience has been expanded ever since, and they have been proposed as a means to promote mental well-being [5] or even as an automated social skills trainer for people with autism spectrum disorders [6].…”

Section: Related Workmentioning

confidence: 99%

A Context-Aware Conversational Agent in the Rehabilitation Domain

et al. 2019

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Conversational agents are reshaping our communication environment and have the potential to inform and persuade in new and effective ways. In this paper, we present the underlying technologies and the theoretical background behind a health-care platform dedicated to supporting medical stuff and individuals with movement disabilities and to providing advanced monitoring functionalities in hospital and home surroundings. The framework implements an intelligent combination of two research areas: (1) sensor- and camera-based monitoring to collect, analyse, and interpret people behaviour and (2) natural machine–human interaction through an apprehensive virtual assistant benefiting ailing patients. In addition, the framework serves as an important assistant to caregivers and clinical experts to obtain information about the patients in an intuitive manner. The proposed approach capitalises on latest breakthroughs in computer vision, sensor management, speech recognition, natural language processing, knowledge representation, dialogue management, semantic reasoning, and speech synthesis, combining medical expertise and patient history.

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Section: Related Workmentioning

confidence: 99%

A Context-Aware Conversational Agent in the Rehabilitation Domain

et al. 2019

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“…The speech signal processing aims to efficiently and accurately transform the acoustic speech signal for use in automatic systems. The extensive development of speech processing research demonstrates the effort to improve the performance of speech recognition systems for practical applications (Bellegarda and Monz 2016;Silva and Serra 2014). The use of such systems allows autonomy in areas as telephony, in which service requests are directed by voice commands (Cardoso et al 2010); in automotive engineering, by driving devices inside the cars (Qian et al 2009;Hua and Ng 2010;Li et al 2013); in computer systems, through computer utility programs, in addition to robotic application (Koo et al 2014) and in residential and hospital automation for accessibility of people with locomotive and visual disabilities (Gnanasekar et al 2012;Singh and Yadav 2015).…”

Section: Motivation and Justificationmentioning

confidence: 99%

Hierarchical Expert Neural Network System for Speech Recognition

Rocha

Silva

Barros

2019

J Control Autom Electr Syst

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This work proposes a hierarchical architecture composed of a expert neural network set based on the ensemble method with dynamic selection of classifiers for application in speech recognition systems. Therefore, 30 commands in the Brazilian Portuguese language were coded by a two-dimensional time matrix, resulting from the application of the discrete cosine transformation in the mel-cepstral coefficients. These patterns were modified by means of a nonlinear transformation to a high-dimensionality space through a set of Gaussian radial basis functions (GRBFs) parameterized with the centroid and covariance characteristics of the classes. The classification was made through the dynamic classifier selection approach, in which multilayer perceptron and learning vector quantization configurations were analyzed to constitute the multiple classifiers specialized in the subdivisions made in the total of classes to be recognized. Then, given a new test pattern, the GRBF that presents the highest value of the receptive field in relation to the input feature vector indicates the class to which the pattern is nearer, thus directing to the expert neural network that provides the final result of classification based on the local accuracy.

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“…The ultimate goal ever since is to develop algorithms which can automatically learn from data, hence can improve with experience over time without any human-in-the-loop. Most colleagues from the ML community are concentrating on automatic Machine Learning (aML), with the grand goal of excluding humans, hence to make it fully automatic and best practice real-world examples can be found in speech processing [43], recommender systems [44], or autonomous vehicles [45], just to mention a few.…”

Section: Research Track 2 Ml: Machine Learning Algorithmsmentioning

confidence: 99%

Machine Learning for Health Informatics

Holzinger¹

2016

Lecture Notes in Computer Science

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Abstract. Machine Learning (ML) studies algorithms which can learn from data to gain knowledge from experience and to make decisions and predictions. Health Informatics (HI) studies the effective use of probabilistic information for decision making. The combination of both has greatest potential to rise quality, efficacy and efficiency of treatment and care. Health systems worldwide are confronted with "big data" in high dimensions, where the inclusion of a human is impossible and automatic ML (aML) show impressive results. However, sometimes we are confronted with complex data, "little data", or rare events, where aMLapproaches suffer of insufficient training samples. Here interactive ML (iML) may be of help, particularly with a doctor-in-the-loop, e.g. in subspace clustering, k-Anonymization, protein folding and protein design. However, successful application of ML for HI needs an integrated approach, fostering a concerted effort of four areas: (1) data science, (2) algorithms (with focus on networks and topology (structure), and entropy (time), (3) data visualization, and last but not least (4) privacy, data protection, safety & security.

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State of the art in statistical methods for language and speech processing

Cited by 33 publications

References 76 publications

A Context-Aware Conversational Agent in the Rehabilitation Domain

A Context-Aware Conversational Agent in the Rehabilitation Domain

Hierarchical Expert Neural Network System for Speech Recognition

Machine Learning for Health Informatics

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