A Channel-Pruned and Weight-Binarized Convolutional Neural Network for Keyword Spotting

Lyu, Jiancheng; Sheen, Spencer

doi:10.1007/978-3-030-38364-0_22

Cited by 2 publications

(1 citation statement)

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“…4d), with 256 filters (f) and a kernel size of 3 (k). A one-dimensional max pooling layer with pooling size of 3 (p) was evaluated between the two convolutional layers in order to help remove variability in the time-frequency domain that exists due to speech variability within each recording [36]. Next, four FC hidden layers with reducing dimensionality (1024-512-256-128 units) were used and followed by the output layer.…”

Section: ) Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Estimating Autism Severity in Young Children From Speech Signals Using a Deep Neural Network

et al. 2020

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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that involves difficulties in social communication. Previous research has demonstrated that these difficulties are apparent in the way ASD children speak, indicating that it may be possible to estimate ASD severity using quantitative features of speech. Here, we extracted a variety of prosodic, acoustic, and conversational features from speech recordings of Hebrew speaking children who completed an Autism Diagnostic Observation Schedule (ADOS) assessment. Sixty features were extracted from the recordings of 72 children and 21 of the features were significantly correlated with the children's ADOS scores. Positive correlations were found with pitch variability and Zero Crossing Rate (ZCR), while negative correlations were found with the speed and number of vocal responses to the clinician, and the overall number of vocalizations. Using these features, we built several Deep Neural Network (DNN) algorithms to estimate ADOS scores and compared their performance with Linear Regression and Support Vector Regression (SVR) models. We found that a Convolutional Neural Network (CNN) yielded the best results. This algorithm predicted ADOS scores with a mean RMSE of 4.65 and a mean correlation of 0.72 with the true ADOS scores when trained and tested on different sub-samples of the available data. Automated algorithms with the ability to predict ASD severity in a reliable and sensitive manner have the potential of revolutionizing early ASD identification, quantification of symptom severity, and assessment of treatment efficacy.

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Section: ) Convolutional Neural Network (Cnn)mentioning

confidence: 99%