John H. L. Hansen

Also published as: John H.L. Hansen


Activity focused Speech Recognition of Preschool Children in Early Childhood Classrooms
Satwik Dutta | Dwight Irvin | Jay Buzhardt | John H.L. Hansen
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

A supportive environment is vital for overall cognitive development in children. Challenges with direct observation and limitations of access to data driven approaches often hinder teachers or practitioners in early childhood research to modify or enhance classroom structures. Deploying sensor based tools in naturalistic preschool classrooms will thereby help teachers/practitioners to make informed decisions and better support student learning needs. In this study, two elements of eco-behavioral assessment: conversational speech and real-time location are fused together. While various challenges remain in developing Automatic Speech Recognition systems for spontaneous preschool children speech, efforts are made to develop a hybrid ASR engine reporting an effective Word-Error-Rate of 40%. The ASR engine further supports recognition of spoken words, WH-words, and verbs in various activity learning zones in a naturalistic preschool classroom scenario. Activity areas represent various locations within the physical ecology of an early childhood setting, each of which is suited for knowledge and skill enhancement in young children. Capturing children’s communication engagement in such areas could help teachers/practitioners fine-tune their daily activities, without the need for direct observation. This investigation provides evidence of the use of speech technology in educational settings to better support such early childhood intervention.

Fearless Steps APOLLO: Advanced Naturalistic Corpora Development
John H.L. Hansen | Aditya Joglekar | Szu-Jui Chen | Meena Chandra Shekar | Chelzy Belitz
Proceedings of the 2nd Workshop on Novel Incentives in Data Collection from People: models, implementations, challenges and results within LREC 2022

In this study, we present the Fearless Steps APOLLO Community Resource, a collection of audio and corresponding meta-data diarized from the NASA Apollo Missions. Massive naturalistic speech data which is time-synchronized, without any human subject privacy constraints is very rare and difficult to organize, collect, and deploy. The Apollo Missions Audio is the largest collection of multi-speaker multi-channel data, where over 600 personnel are communicating over multiple missions to achieve strategic space exploration goals. A total of 12 manned missions over a six-year period produced extensive 30-track 1-inch analog tapes containing over 150,000 hours of audio. This presents the wider research community a unique opportunity to extract multi-modal knowledge in speech science, team cohesion and group dynamics, and historical archive preservation. We aim to make this entire resource and supporting speech technology meta-data creation publicly available as a Community Resource for the development of speech and behavioral science. Here we present the development of this community resource, our outreach efforts, and technological developments resulting from this data. We finally discuss the planned future directions for this community resource.


Multivariate Cepstral Feature Compensation on Band-limited Data for Robust Speech Recognition
Nicolas Morales | Doroteo T. Toledano | John H. L. Hansen | Javier Garrido
Proceedings of the 16th Nordic Conference of Computational Linguistics (NODALIDA 2007)