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Task and language in Spanish–English narratives (Wofford et al., 2022) ...
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Task and language in Spanish–English narratives (Wofford et al., 2022) ...
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Measuring and Comparing Social Bias in Static and Contextual Word Embeddings
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In: Dissertations (2022)
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A Corpus-Based Sentence Classifier for Entity–Relationship Modelling
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In: Electronics; Volume 11; Issue 6; Pages: 889 (2022)
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A Quantum Language-Inspired Tree Structural Text Representation for Semantic Analysis
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In: Mathematics; Volume 10; Issue 6; Pages: 914 (2022)
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An Unsupervised Approach to Structuring and Analyzing Repetitive Semantic Structures in Free Text of Electronic Medical Records
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In: Journal of Personalized Medicine; Volume 12; Issue 1; Pages: 25 (2022)
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Text Mining from Free Unstructured Text: An Experiment of Time Series Retrieval for Volcano Monitoring
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3503 (2022)
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Detection of Chinese Deceptive Reviews Based on Pre-Trained Language Model
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3338 (2022)
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Automated Customer Complaint Processing for Water Utilities Based on Natural Language Processing—Case Study of a Dutch Water Utility
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In: Water; Volume 14; Issue 4; Pages: 674 (2022)
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MLLP-VRAIN Spanish ASR Systems for the Albayzín-RTVE 2020 Speech-to-Text Challenge: Extension
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In: Applied Sciences; Volume 12; Issue 2; Pages: 804 (2022)
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Abstract:
This paper describes the automatic speech recognition (ASR) systems built by the MLLP-VRAIN research group of Universitat Politècnica de València for the Albayzín-RTVE 2020 Speech-to-Text Challenge, and includes an extension of the work consisting of building and evaluating equivalent systems under the closed data conditions from the 2018 challenge. The primary system (p-streaming_1500ms_nlt) was a hybrid ASR system using streaming one-pass decoding with a context window of 1.5 seconds. This system achieved 16.0% WER on the test-2020 set. We also submitted three contrastive systems. From these, we highlight the system c2-streaming_600ms_t which, following a similar configuration as the primary system with a smaller context window of 0.6 s, scored 16.9% WER points on the same test set, with a measured empirical latency of 0.81 ± 0.09 s (mean ± stdev). That is, we obtained state-of-the-art latencies for high-quality automatic live captioning with a small WER degradation of 6% relative. As an extension, the equivalent closed-condition systems obtained 23.3% WER and 23.5% WER, respectively. When evaluated with an unconstrained language model, we obtained 19.9% WER and 20.4% WER; i.e., not far behind the top-performing systems with only 5% of the full acoustic data and with the extra ability of being streaming-capable. Indeed, all of these streaming systems could be put into production environments for automatic captioning of live media streams.
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Keyword:
automatic speech recognition; natural language processing; streaming
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URL: https://doi.org/10.3390/app12020804
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Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages
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In: Future Internet; Volume 14; Issue 3; Pages: 69 (2022)
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Using Conceptual Recurrence and Consistency Metrics for Topic Segmentation in Debate
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In: Applied Sciences; Volume 12; Issue 6; Pages: 2952 (2022)
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Connecting Text Classification with Image Classification: A New Preprocessing Method for Implicit Sentiment Text Classification
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In: Sensors; Volume 22; Issue 5; Pages: 1899 (2022)
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Predicting Institution Outcomes for Inter Partes Review (IPR) Proceedings at the United States Patent Trial & Appeal Board by Deep Learning of Patent Owner Preliminary Response Briefs
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3656 (2022)
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Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 9; Pages: 5705 (2022)
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FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning
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In: Applied Sciences; Volume 12; Issue 6; Pages: 3130 (2022)
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A Dynamic Attention and Multi-Strategy-Matching Neural Network Based on Bert for Chinese Rice-Related Answer Selection
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In: Agriculture; Volume 12; Issue 2; Pages: 176 (2022)
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Correcting Diacritics and Typos with a ByT5 Transformer Model
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2636 (2022)
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