The 11th International Conference on Information Sciences, Signal Processing and their Applications: Main Tracks Towards Unsupervised Speech Processing

Citations in the corpus

• Effectiveness of self-supervised pre-training for speech recognition (2019, methodology): “Our work is inspired by research efforts reducing the dependence on labeled data for building ASR systems through unsupervised unit discovery and acoustic representation leaning [5, 6, 7, 8, 9], and through multi- and cross-lingual transfer learning in low-resource conditions [46, 47, 48, 49, 50, 51”
• Semi-Supervised Speech Recognition via Local Prior Matching (2020, background): “Instead, humans possess an inherent ability to learn from vast quantities of unlabeled speech (Chomsky, 1986; Kuhl, 2004; Glass, 2012; Dupoux, 2018).”
• Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better (2021, unknown): “As demonstrated across NLP [47, 78], Vision [32, 120], Speech [60], etc., Self-Supervision is indeed not just helpful in speeding and improving convergence, but also enabling achieving high quality in tasks where it was intractable to get enough labeled samples.”
• A Factorial Deep Markov Model for Unsupervised Disentangled Representation Learning from Speech (2019, background): “Our interest in unsupervised speech processing stems from the desire to depart from expert based, fully supervised automatic speech recognition systems to the decipher-based scenario [1] where unlabeled speech and non-parallel text are available.”
• Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval (2015, background): “models and a lexicon for the automatically discovered acoustic patterns [292] (bottom left corner).”
• Effectiveness of Self-Supervised Pre-Training for ASR (2020, background): “The practical need for building ASR systems for new conditions with limited resources spurred a lot of work focused on unsupervised speech recognition and representation learning [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], in addition to semi- and weakly-supervised learning techniques to reduce th”
• Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching (2018, methodology): “We compare our results (obtained with matching LM) with several unsupervised phoneme segmentation methods (Dusan and Rabiner, 2006; Qiao et al., 2008; Lee and Glass, 2012; Rasanen, 2014; Hoang and Wang, 2015; Michel et al., 2016; Wang et al., 2017).”
• Segmental acoustic indexing for zero resource keyword search (2015, background): “As a result, the zero-resource setting, in which detailed annotations are unavailable and linguistic structure must be discovered without the aid of training data, has attracted attention both in the speech processing community [2] and among scientists interested in human language acquisition [3].”