11,339 research outputs found
A Review on Emotion Recognition Algorithms using Speech Analysis
In recent years, there is a growing interest in speech emotion recognition (SER) by analyzing input speech. SER can be considered as simply pattern recognition task which includes features extraction, classifier, and speech emotion database. The objective of this paper is to provide a comprehensive review on various literature available on SER. Several audio features are available, including linear predictive coding coefficients (LPCC), Mel-frequency cepstral coefficients (MFCC), and Teager energy based features. While for classifier, many algorithms are available including hidden Markov model (HMM), Gaussian mixture model (GMM), vector quantization (VQ), artificial neural networks (ANN), and deep neural networks (DNN). In this paper, we also reviewed various speech emotion database. Finally, recent related works on SER using DNN will be discussed
DeepCare: A Deep Dynamic Memory Model for Predictive Medicine
Personalized predictive medicine necessitates the modeling of patient illness
and care processes, which inherently have long-term temporal dependencies.
Healthcare observations, recorded in electronic medical records, are episodic
and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural
network that reads medical records, stores previous illness history, infers
current illness states and predicts future medical outcomes. At the data level,
DeepCare represents care episodes as vectors in space, models patient health
state trajectories through explicit memory of historical records. Built on Long
Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle
irregular timed events by moderating the forgetting and consolidation of memory
cells. DeepCare also incorporates medical interventions that change the course
of illness and shape future medical risk. Moving up to the health state level,
historical and present health states are then aggregated through multiscale
temporal pooling, before passing through a neural network that estimates future
outcomes. We demonstrate the efficacy of DeepCare for disease progression
modeling, intervention recommendation, and future risk prediction. On two
important cohorts with heavy social and economic burden -- diabetes and mental
health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare
trajectories from medical records: A deep learning approach
Complex sequencing rules of birdsong can be explained by simple hidden Markov processes
Complex sequencing rules observed in birdsongs provide an opportunity to
investigate the neural mechanism for generating complex sequential behaviors.
To relate the findings from studying birdsongs to other sequential behaviors,
it is crucial to characterize the statistical properties of the sequencing
rules in birdsongs. However, the properties of the sequencing rules in
birdsongs have not yet been fully addressed. In this study, we investigate the
statistical propertiesof the complex birdsong of the Bengalese finch (Lonchura
striata var. domestica). Based on manual-annotated syllable sequences, we first
show that there are significant higher-order context dependencies in Bengalese
finch songs, that is, which syllable appears next depends on more than one
previous syllable. This property is shared with other complex sequential
behaviors. We then analyze acoustic features of the song and show that
higher-order context dependencies can be explained using first-order hidden
state transition dynamics with redundant hidden states. This model corresponds
to hidden Markov models (HMMs), well known statistical models with a large
range of application for time series modeling. The song annotation with these
models with first-order hidden state dynamics agreed well with manual
annotation, the score was comparable to that of a second-order HMM, and
surpassed the zeroth-order model (the Gaussian mixture model (GMM)), which does
not use context information. Our results imply that the hierarchical
representation with hidden state dynamics may underlie the neural
implementation for generating complex sequences with higher-order dependencies
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