Statistical Language and Speech Processing

8th International Conference, SLSP 2020, Cardiff, UK, October 14¿16, 2020, Proceedings
Herausgegeben:Espinosa-Anke, Luis; Martín-Vide, Carlos; Spasic, Irena

• Broschiertes Buch

Produktbeschreibung

This book constitutes the proceedings of the 8th International Conference on Statistical Language and Speech Processing, SLSP 2020, held in Cardiff, UK, in October 2020.
The 13 full papers presented together with one invited paper in this volume were carefully reviewed and selected from 25 submissions. They papers cover the wide spectrum of statistical methods that are currently in use in computational language or speech processing.

Produktdetails

• Lecture Notes in Computer Science 12379
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-59429-9
• 1st ed. 2020
• Seitenzahl: 196
• Erscheinungstermin: 26. September 2020
• Englisch
• Abmessung: 235mm x 155mm x 11mm
• Gewicht: 306g
• ISBN-13: 9783030594299
• ISBN-10: 3030594297
• Artikelnr.: 59974626

Inhaltsangabe

Grapheme-to-Phoneme Transduction for Cross-Language ASR.- Language Processing.-Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systems.- FacTweet: Profiling Fake News Twitter Accounts.- Named Entity Recognition for Icelandic: Annotated Corpus and Models.- BERT-based Sentiment Analysis using Distillation.- A Cognitive Approach to Parsing with Neural Networks.- S-capade: Spelling Correction Aimed at Particularly Deviant Errors.- Exploring Parameter Sharing Techniques for Cross-Lingual and Cross-Task Supervision.- A Discourse-Informed Approach for Cost-E ective Extractive Summarization.- Towards eXplainable AI in Text Features Engineering for Concept Recognition.- A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification.- ANN-MLP Classifier of Native and Nonnative Speakers Using Speech Rhythm Cues.- Deep Variational Metric Learning for Transfer of Expressivity in Multispeaker Text to Speech.- Generative Adversarial Network-based Semi-Supervised Learning for Pathological Speech Classfication.