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Acoustic analyses of infant vocalizations are valuable for research on speechdevelopment as well as applications in sound classification. Previous studieshave focused on measures of acoustic features based on theories of speechprocessing, such spectral and cepstrum-based analyses. More recently,end-to-end models of deep learning have been developed to take raw speechsignals (acoustic waveforms) as inputs and convolutional neural network layersto learn representations of speech sounds based on classification tasks. Weapplied a recent end-to-end model of sound classification to analyze alarge-scale database of labeled infant and adult vocalizations recorded innatural settings outside the lab with no control over recording conditions. Themodel learned basic classifications like infant versus adult vocalizations,infant speech-related versus non-speech vocalizations, and canonical versusnon-canonical babbling. The model was trained on recordings of infants rangingfrom 3 to 18 months of age, and classification accuracy changed with age asspeech became more distinct and babbling became more speech-like. Further workis needed to validate and explore the model and dataset, but our results showhow deep learning can be used to measure and investigate speech acquisition anddevelopment, with potential applications in speech pathology and infantmonitoring.

InfantNet: A Deep Neural Network for Analyzing Infant Vocalizations