BACKGROUND: To characterize acoustic features of an infant's cry and use machine learning to provide an objective measurement of behavioral state in a cry-translator. To apply the cry-translation algorithm to colic hypothesizing that these cries sound painful. METHODS: Assessment of 1000 cries in a mobile app (ChatterBaby TM). Training a cry-translation algorithm by evaluating >6000 acoustic features to predict whether infant cry was due to a pain (vaccinations, ear-piercings), fussy, or hunger states. Using the algorithm to predict the behavioral state of infants with reported colic. RESULTS: The cry-translation algorithm was 90.7% accurate for identifying pain cries, and achieved 71.5% accuracy in discriminating cries from fussiness, hunger, or pain. The ChatterBaby cry-translation algorithm overwhelmingly predicted that colic cries were most likely from pain, compared to fussy and hungry states. Colic cries had average pain ratings of 73%, significantly greater than the pain measurements found in fussiness and hunger (p < 0.001, 2-sample t test). Colic cries outranked pain cries by measures of acoustic intensity, including energy, length of voiced periods, and fundamental frequency/pitch, while fussy and hungry cries showed reduced intensity measures compared to pain and colic. CONCLUSIONS: Acoustic features of cries are consistent across a diverse infant population and can be utilized as objective markers of pain, hunger, and fussiness. The ChatterBaby algorithm detected significant acoustic similarities between colic and painful cries, suggesting that they may share a neuronal pathway.
Text-independent speaker recognition using short utterances is a highly challenging task due to the large variation and content mismatch between short utterances. I-vector and probabilistic linear discriminant analysis (PLDA) based systems have become the standard in speaker verification applications, but they are less effective with short utterances. To address this issue, we propose a novel method, which trains a convolutional neural network (CNN) model to map the i-vectors extracted from short utterances to the corresponding long-utterance i-vectors. In order to simultaneously learn the representation of the original short-utterance i-vectors and fit the target long-version ivectors, we jointly train a supervised-regression model with an autoencoder using CNNs. The trained CNN model is then used to generate the mapped version of short-utterance i-vectors in the evaluation stage. We compare our proposed CNNbased joint mapping method with a GMM-based joint modeling method under matched and mismatched PLDA training conditions. Experimental results using the NIST SRE 2008 dataset show that the proposed technique achieves up to 30% relative improvement under duration mismatched PLDA-training conditions and outperforms the GMM-based method. The improved systems also perform better compared with the matched-length PLDA training condition using short utterances.
Increased hemodynamic latency in the visual cortex predicted impaired cognitive function (p<0.05), holding constant demographic and cerebrovascular risk. Increased alcohol use was associated with reduced overall cognitive function (Full Scale IQ 2.8 pts, p<0.05), while cardiac disorders (Full Scale IQ 3.3 IQ pts; p<0.05), high cholesterol (Full Scale IQ 3.9 pts; p<0.05), and years of education (2 IQ pts/year; p<0.001) were associated with higher general cognitive ability. Increased hemodynamic latency was associated with reduced executive functioning (p<0.05) as well as deficits in verbal concept formation (p<0.05) and the ability to synthesize and analyze abstract visual information (p<0.01).Hemodynamic latency is associated with reduced cognitive ability across the lifespan, independently of other demographic and cerebrovascular risk factors.
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