A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Speech Synthesis and Recognition
Speech Synthesis and Recognition
Robust boosting and its relation to bagging
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Acceleration schemes with application to the EM algorithm
Computational Statistics & Data Analysis
Automatic speech recognition and speech variability: A review
Speech Communication
Invited paper: Automatic speech recognition: History, methods and challenges
Pattern Recognition
Multimedia Content Analysis: Theory and Applications
Multimedia Content Analysis: Theory and Applications
An improved Akaike information criterion for state-space model selection
Computational Statistics & Data Analysis
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
A combined classifier of cry units with new acoustic attributes
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Pathological infant cry analysis using wavelet packet transform and probabilistic neural network
Expert Systems with Applications: An International Journal
IEEE Transactions on Audio, Speech, and Language Processing
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We make use of information inside infant's cry signal in order to identify the infant's psychological condition. Gaussian mixture models (GMMs) are applied to distinguish between healthy full-term and premature infants, and those with specific medical problems available in our cry database. Cry pattern for each pathological condition is created by using adapted boosting mixture learning (BML)method to estimatemixture model parameters. In the first experiment, test results demonstrate that the introduced adapted BML method for learning of GMMs has a better performance than conventional EM-based reestimation algorithm as a reference system in multipathological classification task. This newborn cry-based diagnostic system (NCDS) extracted Melfrequency cepstral coefficients (MFCCs) as a feature vector for cry patterns of newborn infants. In binary classification experiment, the system discriminated a test infant's cry signal into one of two groups, namely, healthy and pathological based on MFCCs. The binary classifier achieved a true positive rate of 80.77% and a true negative rate of 86.96% which show the ability of the system to correctly identify healthy and diseased infants, respectively.