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XLS-R fine-tuning on noisy word boundaries for unsupervised speech segmentation into words., , , and . EMNLP (Findings), page 12103-12112. Association for Computational Linguistics, (2023)Stop: A Dataset for Spoken Task Oriented Semantic Parsing., , , , , , , , , and 5 other author(s). SLT, page 991-998. IEEE, (2022)Evaluating the Reliability of Acoustic Speech Embeddings., , , and . INTERSPEECH, page 4621-4625. ISCA, (2020)Fine-Tuning Strategies for Faster Inference Using Speech Self-Supervised Models: A Comparative Study., , , , and . ICASSP Workshops, page 1-5. IEEE, (2023)Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning., , , and . INTERSPEECH, page 2123-2127. ISCA, (2022)The Zero Resource Speech Challenge 2020: Discovering Discrete Subword and Word Units., , , , , , , , and . INTERSPEECH, page 4831-4835. ISCA, (2020)Do Coarser Units Benefit Cluster Prediction-Based Speech Pre-Training?, , , , , , , , and . ICASSP, page 1-5. IEEE, (2023)Generative Spoken Language Model based on continuous word-sized audio tokens., , , , , , and . EMNLP, page 3008-3028. Association for Computational Linguistics, (2023)The Zero Resource Speech Challenge 2019: TTS Without T., , , , , , , , , and 3 other author(s). INTERSPEECH, page 1088-1092. ISCA, (2019)