child automatic speech recognition to measure academically productive talk in the classroom
Teachers and instructors in K-12 STEM classrooms are moving towards an educational system that encourages children to discuss ideas, actively listen to one another, and to construct arguments and explanations based on evidence and reasoning (Michaels et al. 2002). This approach has been shown to particularly help girls and other members of groups underrepresented in STEM to gain confidence in their mathematical abilities (Suresh et al 2019).
To facilitate “academically productive” talk, teachers encourage the use of “talk moves”: phrases and questions that turn classrooms into knowledge-building communities where students intellectually engage with each other’s ideas (Michaels et al. 2012). Increasingly, teachers are using recordings of classroom sessions to monitor the use of talk moves in the classroom. However, transcribing classroom sessions is expensive and time-consuming.
The TalkBack platform from the University of Colorado (CU) Boulder avoids this expense by providing teachers with feedback on their instruction by using adult speech recognition to transcribe the teachers’ speech and identify certain talk moves in class.
Child speech recognition is a much more difficult task than adult ASR because of the physiological and behavioural differences between adults and children. In order to monitor talk moves it is necessary to have accurate transcriptions of both the teachers’ and the students’ speech. Due to the lack of available child ASR, the TalkBack system is as yet unable to accurately transcribe the students’ speech and report on their contribution to academically productive talk during classroom sessions.
Automatic speech recognition (ASR) is the process by which a computer can transcribe the linguistic contents of an audio file. In CARNIVAL I focus on one particular application of child ASR in elementary school science, technology, engineering and mathematics (STEM) education.
Automatic speech recognition (ASR) is the process by which a computer can transcribe the linguistic contents of an audio file. CARNIVAL will focus on using the latest advances in machine learning and artificial intelligence to improve ASR technology for elementary school child speech in the classroom. By collaborating with the Institute of Cognitive Science (ICS) at CU Boulder, I propose to integrate a child ASR system into the TalkBack platform, which will be used by K-12 STEM teachers to monitor their use of academically productive talk in class. The ability to transcribe the students’ speech is integral to the system’s ability to classify and monitor talk moves, and will add a new layer of functionality to this platform.
CARNIVAL 