DIANA, an end-to-end computational model of spoken word recognition, was previously used to simulate auditory lexical decision experiments in Dutch. A single test conducted for North American English showed promising results as well. However, this simulation used a relatively small amount of data collected in the pilot phase of the Massive Auditory Lexical Decision (MALD) project. Additionally, already existing acoustic models were implemented. In this paper, we expand the analysis of MALD data by including a larger sample of both stimuli and participants. Acknowledging that most speech humans hear is conversational speech, we also test new acoustic models created using spontaneous speech corpora. Simulations successfully replicate expected trends in word competition and show plausible competitors as the signal unfolds, but acoustic model accuracy should be improved. Despite the number of responses per word being relatively small (never more than five), correlations between model estimates and participants' responses are moderate. Future directions in acoustic model training and simulating MALD data are discussed.
Cite as: Nenadić, F., ten Bosch, L., Tucker, B.V. (2018) Implementing DIANA to Model Isolated Auditory Word Recognition in English. Proc. Interspeech 2018, 3772-3776, doi: 10.21437/Interspeech.2018-2081
@inproceedings{nenadic18_interspeech,
author={Filip Nenadić and Louis {ten Bosch} and Benjamin V. Tucker},
title={{Implementing DIANA to Model Isolated Auditory Word Recognition in English}},
year=2018,
booktitle={Proc. Interspeech 2018},
pages={3772--3776},
doi={10.21437/Interspeech.2018-2081}
}