Dirichlet process mixture model based on topologically augmented signal representation for clustering infant vocalizations


  • Bonafos Guillem
  • Bourot Clara
  • Pudlo Pierre
  • Freyermuth Jean-Marc
  • Reboul Laurence
  • Tronçon Samuel
  • Rey Arnaud


  • Clustering
  • Bayesian non-parametric
  • Dirichlet process
  • Mixture model
  • Topologically-augmented machine learning
  • TDA
  • Babbling
  • Language development
  • Vocalizations

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Based on audio recordings made once a month during the first 12 months of a child's life, we propose a new method for clustering this set of vocalizations. We use a topologically augmented representation of the vocalizations, employing two persistence diagrams for each vocalization: one computed on the surface of its spectrogram and one on the Takens' embeddings of the vocalization. A synthetic persistent variable is derived for each diagram and added to the MFCCs (Mel-frequency cepstral coefficients). Using this representation, we fit a non-parametric Bayesian mixture model with a Dirichlet process prior to model the number of components. This procedure leads to a novel data-driven categorization of vocal productions. Our findings reveal the presence of 8 clusters of vocalizations, allowing us to compare their temporal distribution and acoustic profiles in the first 12 months of life.

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