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We introduce a method for measuring the correspondence between low-level speech features and human perception, using a cognitive model of speech perception implemented directly on speech recordings. We evaluate two speaker normalization techniques using this method and find that in both cases, speech features that are normalized across speakers predict human data better than unnormalized speech features, consistent with previous research. Results further reveal differences across normalization methods in how well each predicts human data. This work provides a new framework for evaluating low-level representations of speech on their match to human perception, and lays the groundwork for creating more ecologically valid models of speech perception.

A framework for evaluating speech representations

Richter, Caitlin; Feldman, Naomi H.; Salgado, Harini; Jansen, Aren (August 2016, Proceedings of the Annual Conference of the Cognitive Science Society)

Listeners track distributions of speech sounds along perceptual dimensions. We introduce a method for evaluating hypotheses about what those dimensions are, using a cognitive model whose prior distribution is estimated directly from speech recordings. We use this method to evaluate two speaker normalization algorithms against human data. Simulations show that representations that are normalized across speakers predict human discrimination data better than unnormalized representations, consistent with previous research. Results further reveal differences across normalization methods in how well each predicts human data. This work provides a framework for evaluating hypothesized representations of speech and lays the groundwork for testing models of speech perception on natural speech recordings from ecologically valid settings.

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