Detection of infant emotional outbursts, such as crying, in large corpora of recorded infant speech, is essential to the study of dyadic social process, by which infants learn to identify and regulate their own emotions. Such large corpora now exist with the advent of LENA speech monitoring systems, but are not labeled for emotional outbursts. This paper reports on our efforts to manually code child utterances as being of type”laugh”,”cry”,”fuss”,”babble” and”hiccup”, and to develop algorithms capable of performing the same task automatically. Human labelers achieve much higher rates of inter-coder agreement for some of these categories than for others. Linear discriminant analysis (LDA) achieves better accuracy on tokens that have been coded by two human labelers than on tokens that have been coded by only one labeler, but the difference is not as much as we expected, suggesting that the acoustic and contextual features being used by human labelers are not yet available to the LDA. Convolutional neural network and hidden markov model achieve better accuracy than LDA, but worse F-score, because they over-weight the prior. Discounting the transition probability does not solve the problem.

Original languageEnglish (US)
Pages (from-to)242-246
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - 2018
Event19th Annual Conference of the International Speech Communication, INTERSPEECH 2018 - Hyderabad, India
Duration: Sep 2 2018Sep 6 2018


  • Convolutional neural network
  • Hidden markov model
  • Infant emotional outbursts
  • Infant vocalizations
  • Linear discriminant analysis

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modeling and Simulation


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