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Title: A DNN-HMM-DNN Hybrid Model for Discovering Word-Like Units from Spoken Captions and Image Regions
Discovering word-like units without textual transcriptions is an important step in low-resource speech technology. In this work,we demonstrate a model inspired by statistical machine translation and hidden Markov model/deep neural network (HMM-DNN) hybrid systems. Our learning algorithm is capable of discovering the visual and acoustic correlates of distinct words in an unknown language by simultaneously learning the map-ping from image regions to concepts (the first DNN), the map-ping from acoustic feature vectors to phones (the second DNN),and the optimum alignment between the two (the HMM). In the simulated low-resource setting using MSCOCO and Speech-COCO datasets, our model achieves 62.4 % alignment accuracy and outperforms the audio-only segmental embedded GMM approach on standard word discovery evaluation metrics.  more » « less
Award ID(s):
1910319
NSF-PAR ID:
10273579
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Interspeech
Page Range / eLocation ID:
1456 to 1460
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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