A graph-theoretic approach to identifying acoustic cues for speech sound categorization.

Human speech contains a wide variety of acoustic cues that listeners must map onto distinct phoneme categories. The large amount of information contained in these cues contributes to listeners' remarkable ability to accurately recognize speech across a variety of contexts. However, these cues vary across talkers, both in terms of how specific cue values map onto different phonemes and in terms of which cues individual talkers use most consistently to signal specific phonological contrasts. This creates a challenge for models that aim to characterize the information used to recognize speech. How do we balance the need to account for variability in speech sounds across a wide range of talkers with the need to avoid overspecifying which acoustic cues describe the mapping from speech sounds onto phonological distinctions? We present an approach using tools from graph theory that addresses this issue by creating networks describing connections between individual talkers and acoustic cues and by identifying subgraphs within these networks. This allows us to reduce the space of possible acoustic cues that signal a given phoneme to a subset that still accounts for variability across talkers, simplifying the model and providing insights into which cues are most relevant for specific phonemes. Classifiers trained on the subset of cue dimensions identified in the subgraphs provide fits to listeners' categorization that are similar to those obtained for classifiers trained on all cue dimensions, demonstrating that the subgraphs capture the cues necessary to categorize speech sounds.