Atjaunināt sīkdatņu piekrišanu

Man-Machine Speech Communication: 17th National Conference, NCMMSC 2022, Hefei, China, December 1518, 2022, Proceedings 1st ed. 2023 [Mīkstie vāki]

Edited by , Edited by , Edited by , Edited by
  • Formāts: Paperback / softback, 332 pages, height x width: 235x155 mm, weight: 528 g, 86 Illustrations, color; 5 Illustrations, black and white; XI, 332 p. 91 illus., 86 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 1765
  • Izdošanas datums: 11-May-2023
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9819924006
  • ISBN-13: 9789819924004
  • Mīkstie vāki
  • Cena: 73,68 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Standarta cena: 86,69 €
  • Ietaupiet 15%
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
  • Formāts: Paperback / softback, 332 pages, height x width: 235x155 mm, weight: 528 g, 86 Illustrations, color; 5 Illustrations, black and white; XI, 332 p. 91 illus., 86 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 1765
  • Izdošanas datums: 11-May-2023
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9819924006
  • ISBN-13: 9789819924004
This book constitutes the refereed proceedings of the 17th National Conference on Man–Machine Speech Communication, NCMMSC 2022, held in China, in December 2022.

The 21 full papers and 7 short papers included in this book were carefully reviewed and selected from 108 submissions. They were organized in topical sections as follows: MCPN: A Multiple Cross-Perception Network for Real-Time Emotion Recognition in Conversation.- Baby Cry Recognition Based on Acoustic Segment Model, MnTTS2 An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis Dataset.
MCPN: A Multiple Cross-Perception Network for Real-Time Emotion
Recognition in Conversation.- Baby Cry Recognition Based on Acoustic Segment
Model.- A Multi-feature Sets Fusion Strategy with Similar Samples Removal for
Snore Sound Classification.- Multi-Hypergraph Neural Networks for Emotion
Recognition in Multi-Party Conversations.- Using Emoji as an Emotion Modality
in Text-Based Depression Detection.- Source-Filter-Based Generative
Adversarial Neural Vocoder for High Fidelity Speech Synthesis.- Semantic
enhancement framework for robust speech recognition.- Achieving Timestamp
Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model.-
Predictive AutoEncoders are Context-Aware Unsupervised Anomalous Sound
Detectors.- A pipelined framework with serialized output training for
overlapping speech recognition.- Adversarial Training Based on Meta-Learning
in Unseen Domains for Speaker Verification.- Multi-Speaker Multi-Style Speech
Synthesis with Timbre and Style Disentanglement.- Multiple Confidence Gates
for Joint Training of SE and ASR.- Detecting Escalation Level from Speech
with Transfer Learning and Acoustic-Linguistic Information Fusion.-
Pre-training Techniques For Improving Text-to-Speech Synthesis By Automatic
Speech Recognition Based Data Enhancement.- A Time-Frequency Attention
Mechanism with Subsidiary Information for Effective Speech Emotion
Recognition.- Interplay between prosody and syntax-semantics: Evidence from
the prosodic features of Mandarin tag questions.- Improving Fine-grained
Emotion Control and Transfer with Gated Emotion Representations in Speech
Synthesis.- Violence Detection through Fusing Visual Information to Auditory
Scene.- Mongolian Text-to-Speech Challenge under Low-Resource Scenario for
NCMMSC2022.- VC-AUG  Voice Conversion based Data Augmentation for
Text-Dependent Speaker Verication.- Transformer-based potential emotional
relation mining network for emotion recognition in conversation.- FastFoley
Non-Autoregressive Foley Sound Generation Based On Visual Semantics.-
Structured Hierarchical Dialogue Policy with Graph Neural Networks.- Deep
Reinforcement Learning for On-line Dialogue State Tracking.- Dual Learning
for Dialogue State Tracking.- Automatic Stress Annotation and Prediction For
Expressive Mandarin TTS.- MnTTS2 An Open-Source Multi-Speaker Mongolian
Text-to-Speech Synthesis Dataset.