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Modelling human speech recognition using automatic speech recognition paradigms in SpeM

MPG-Autoren
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McQueen,  James M.
Language Comprehension Group, MPI for Psycholinguistics, Max Planck Society;
Decoding Continuous Speech, MPI for Psycholinguistics, Max Planck Society;

Norris,  Dennis
Language Comprehension Group, MPI for Psycholinguistics, Max Planck Society;
Decoding Continuous Speech, MPI for Psycholinguistics, Max Planck Society;

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Scharenborg_2003_modelling.pdf
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Zitation

Scharenborg, O., McQueen, J. M., Ten Bosch, L., & Norris, D. (2003). Modelling human speech recognition using automatic speech recognition paradigms in SpeM. In Proceedings of Eurospeech 2003 (pp. 2097-2100). Adelaide: Causal Productions.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-17B7-D
Zusammenfassung
We have recently developed a new model of human speech recognition, based on automatic speech recognition techniques [1]. The present paper has two goals. First, we show that the new model performs well in the recognition of lexically ambiguous input. These demonstrations suggest that the model is able to operate in the same optimal way as human listeners. Second, we discuss how to relate the behaviour of a recogniser, designed to discover the optimum path through a word lattice, to data from human listening experiments. We argue that this requires a metric that combines both path-based and word-based measures of recognition performance. The combined metric varies continuously as the input speech signal unfolds over time.