Speech recognition by an artificial neural network using findings on the afferent auditory system

Kurogi, Shuichi

doi:10.1007/bf00201985

Cited by 13 publications

(3 citation statements)

References 9 publications

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“…Advanced analytical techniques to assess informative features from these data and model underlying relationships that cannot be modeled with traditional statistical tools could increase the biomechanical research quality, as it has occurred in other areas of knowledge (i.e., speech recognition, disease detection, etc.) (19)(20)(21)(22).…”

mentioning

confidence: 99%

A global view on how local muscular fatigue affects human performance

Goethel

Gonçalves

Brietzke

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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There is a growing interest in scientific literature on identifying how and to what extent interventions applied to a specific body region influence the responses and functions of other seemingly unrelated body regions. To investigate such a construct, it is necessary to have a global multivariate model that considers the interaction among several variables that are involved in a specific task and how a local and acute impairment affects the behavior of the output of such a model. We developed an artificial neural network (ANN)-based multivariate model by using parameters of motor skills obtained from kinematic, postural control, joint torque, and proprioception variables to assess the local fatigue effects of the abductor hip muscles on the functional profile during a single-leg drop landing and a squatting task. Findings suggest that hip abductor muscles’ local fatigue produces a significant effect on a general functional profile, built on different control systems. We propose that expanded and global approaches, such as the one used in this study, have great applicability and have the potential to serve as a tool that guarantees ecological validity of future investigations.

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mentioning

confidence: 99%

A global view on how local muscular fatigue affects human performance

Goethel

Gonçalves

Brietzke

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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“…A number of ANN algorithms and their applications have been widely reported (Rumelhart et al 1986;Lippmann 1987;Huang and Lippmann 1987;Vogl et al 1988). Among the many neural network models, Multi-Layer Perceptrons which are considered the most useful learning models (Rumelhart et al 1986) have been widely used in the medical field (Kurogi 1991;Marmarou et al 1990;Iwata et al 1990;Kelly et al 1990). A new data compression algorithm using artificial neural networks for Digital Holter monitoring, which is a 24-h casette recording system of the electrocardiogram (ECG), was proposed.…”

Section: Neural Networkmentioning

confidence: 98%

Noninvasive diagnosis of coronary artery disease using a neural network algorithm

Akay

1992

Biol. Cybern.

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This study examines the utility of neural networks for detecting coronary artery disease noninvasively by using the clinical examination variables and extracting useful information from the diastolic heart sounds associated with coronary occlusions. It has been widely reported that coronary stenoses produce sounds due to the turbulent blood flow in these vessels. These complex and highly attenuated signals taken from recordings made in both soundproof and noisy rooms were detected and analyzed to provide feature set based on the poles and power spectral density function (PSD) of the Autoregressive (AR) method after Adaptive Line Enhancement (ALE) method. In addition, some physical examination variables such as sex, age, body weight, smoking condition, diastolic pressure, systolic pressure and derivation from them were included in the feature vector. This feature vector was used as the input pattern to the neural network. The analysis was studied on one hundred recordings (63 abnormal, 37 normals). The network correctly identified 84% of the subjects with coronary artery disease and 89% of the normal subjects.

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“…The fact that the system does not achieve greater accuracy is due to the many strange rules of languages for example co-articulation of syllables. Japanese and Finnish may actually be rather more logical in this respect than other languages, for example English. In what might be an imitation of nature, Kurogi [53] used the anatomical and physiological findings on the afferent pathway from the ear to the cortex to develop a neural network that recognizes spoken words, allowing for changes in pitch, timing etc.…”

Section: User Interfacesmentioning

confidence: 99%

Use of artificial intelligence in analytical systems for the clinical laboratory

Jf¹,

Truchaud²,

Ozawa³

et al. 1995

Journal of Analytical Methods in Chemistry

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The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI), both as expert systems and as neural networks. This paper considers the role of software in system operation, control and automation, and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system. In the second part of the paper, examples are given of applications of AI in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories. It is concluded that AI constitutes a collective form of intellectual propery, and that there is a need for better documentation, evaluation and regulation of the systems already being used in clinical laboratories.

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Speech recognition by an artificial neural network using findings on the afferent auditory system

Cited by 13 publications

References 9 publications

A global view on how local muscular fatigue affects human performance

A global view on how local muscular fatigue affects human performance

Noninvasive diagnosis of coronary artery disease using a neural network algorithm

Use of artificial intelligence in analytical systems for the clinical laboratory

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