Children who speak non-standard dialects of English show reduced performance not just in language-oriented topics in school but also in math and science. Technological solutions have been rare exactly because of the nonmainstream nature of their talk, and hence the difficulty in automatically recognizing their speech and responding to it with, for example, computer tutors. In order to work towards overcoming this achievement gap, in this work we investigate African American students’ prosodic inventories in different contexts as a first-step towards building a system that will be able to automatically recognize, and respond to, the dialect in which a child is speaking. We presented children with recordings of a peer (confederate) speaking in either African American English (AAE) or Mainstream American English (MAE) during both a social task and a science task. We found that children showed decreased prosodic variation and peak slopes during speech segments which did not contain AAE features, resulting in more monotone and breathy utterances than when they are speaking in AAE. We also found that children who were speaking with a “peer” who uses AAE have increased articulation rates, energy, and pitch variation. We discuss potential interpretations of these results that are important to the design of a system to support linguistic diversity and decrease the achievement gap.
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