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Unsupervised Word Segmentation and Lexicon Discovery Using Acoustic Word Embeddings.

, , and . IEEE ACM Trans. Audio Speech Lang. Process., 24 (4): 669-679 (2016)

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Acoustic Word Embeddings for Untranscribed Target Languages with Continued Pretraining and Learned Pooling., , , and . CoRR, (2023)Unsupervised Dependency Parsing with Acoustic Cues., and . Trans. Assoc. Comput. Linguistics, (2013)Pre-training on high-resource speech recognition improves low-resource speech-to-text translation., , , , and . NAACL-HLT (1), page 58-68. Association for Computational Linguistics, (2019)Fully unsupervised small-vocabulary speech recognition using a segmental Bayesian model., , and . INTERSPEECH, page 678-682. ISCA, (2015)Unsupervised Word Segmentation and Lexicon Discovery Using Acoustic Word Embeddings., , and . IEEE ACM Trans. Audio Speech Lang. Process., 24 (4): 669-679 (2016)Analyzing ASR Pretraining for Low-Resource Speech-to-Text Translation., , and . ICASSP, page 7909-7913. IEEE, (2020)Classifying topics in speech when all you have is crummy translations., , , and . CoRR, (2019)Semantic Parsing with Bayesian Tree Transducers., , and . ACL (1), page 488-496. The Association for Computer Linguistics, (2012)Which Words Are Hard to Recognize? Prosodic, Lexical, and Disfluency Factors that Increase ASR Error Rates., , and . ACL, page 380-388. The Association for Computer Linguistics, (2008)Modeling Graph Languages with Grammars Extracted via Tree Decompositions., , and . FSMNLP, page 54-62. The Association for Computer Linguistics, (2013)