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Found 4 papers, 1 papers with code
Neural Representations for Modeling Variation in Speech
We show that speech representations extracted from a specific type of neural model (i. e. Transformers) lead to a better match with human perception than two earlier approaches on the basis of phonetic transcriptions and MFCC-based acoustic features.
Morphological Segmentation for Low Resource Languages
This paper describes a new morphology resource created by Linguistic Data Consortium and the University of Pennsylvania for the DARPA LORELEI Program.
Evaluating Low-Level Speech Features Against Human Perceptual Data
We introduce a method for measuring the correspondence between low-level speech features and human perception, using a cognitive model of speech perception implemented directly on speech recordings.
Automatic Speech Recognition (ASR)
Representation Learning
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