Results 41 to 50 of about 545,718 (303)
Classification of emotional speech using spectral pattern features [PDF]
Journal of Artificial Intelligence and Data Mining, 2014 Speech Emotion Recognition (SER) is a new and challenging research area with a wide range of applications in man-machine interactions. The aim of a SER system is to recognize human emotion by analyzing the acoustics of speech sound.
Ali Harimi +3 more
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APLIKASI PENGENALAN UCAPAN DENGAN JARINGAN SYARAF TIRUAN PROPAGASI BALIK UNTUK PENGENDALIAN SMART WHEELCHAIR [PDF]
, 2012 Speech recognition process can be done in many ways one of them with artificial neural networks. In order to be easily understood and to understand, it would require some method of characteristics extraction methods such as by LPC and Fourier ...
Hudhaya , Dewanto Arby
core
Pediatric Blood &Cancer, EarlyView.ABSTRACT Ongoing evidence indicates increased risk of sarcopenic obesity among children and young people (CYP) with acute lymphoblastic leukemia (ALL), often beginning early in treatment, persisting into survivorship. This review evaluates current literature on body composition in CYP with ALL during and after treatment.
Lina A. Zahed +5 more
wiley +1 more source
Environmental Sound Recognition Using Time-Frequency Intersection Patterns
Applied Computational Intelligence and Soft Computing, 2012 Environmental sound recognition is an important function of robots and intelligent computer systems. In this research, we use a multistage perceptron neural network system for environmental sound recognition.
Xuan Guo +5 more
doaj +1 more source
Research on Speech Emotion Recognition Method Based A-CapsNet
Applied Sciences, 2022 Speech emotion recognition is a crucial work direction in speech recognition. To increase the performance of speech emotion detection, researchers have worked relentlessly to improve data augmentation, feature extraction, and pattern formation.
Yingmei Qi, Heming Huang, Huiyun Zhang
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Signal Classification in Quotient Spaces via Globally Optimal Variational Calculus
, 2017 A ubiquitous problem in pattern recognition is that of matching an observed time-evolving pattern (or signal) to a gold standard in order to recognize or characterize the meaning of a dynamic phenomenon.
Chirikjian, Gregory S
core +1 more source
Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines [PDF]
, 2016 Tackling pattern recognition problems in areas such as computer vision, bioinformatics, speech or text recognition is often done best by taking into account task-specific statistical relations between output variables.
De Turck, Filip, Houthooft, Rein
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The Pattern Recognition Methods for Emotion Recognition with Speech Signal [PDF]
Journal of Control, Automation and Systems Engineering, 2006 In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features.
openaire +1 more source
Parent‐to‐Child Information Disclosure in Pediatric Oncology
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Despite professional consensus regarding the importance of open communication with pediatric cancer patients about their disease, actual practice patterns of disclosure are understudied. Extant literature suggests a significant proportion of children are not told about their diagnosis/prognosis, which is purported to negatively ...
Rachel A. Kentor +12 more
wiley +1 more source
Statistical pattern recognition approach to speech segmentation [PDF]
The Journal of the Acoustical Society of America, 1974 It is believed by many speech researchers that there is abundant information contained in the transition segments [J. L. Flanagan, Speech Analysis, Synthesis and Perception (1972), 2nd ed.]. This paper describes an algorithm to recognize different segments of connected speech, e.g., voiced, unvoiced, silence, and transition.
K. Ganesan, Wen C. Lin
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