
Man-Machine Speech Communication
17th National Conference, NCMMSC 2022, Hefei, China, December 15-18, 2022, Proceedings
Springer (Publisher)
Published on 11. May 2023
Book
Paperback/Softback
XI, 332 pages
978-981-99-2400-4 (ISBN)
Description
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.
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.
More details
Series
Edition
1st ed. 2023
Language
English
Place of publication
Singapore
Singapore
Target group
Professional and scholarly
Illustrations
86 farbige Abbildungen, 5 s/w Abbildungen
XI, 332 p. 91 illus., 86 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 19 mm
Weight
522 gr
ISBN-13
978-981-99-2400-4 (9789819924004)
DOI
10.1007/978-981-99-2401-1
Schweitzer Classification
Other editions
Additional editions

Ling Zhenhua | Gao Jianqing | Yu Kai
Man-Machine Speech Communication
17th National Conference, NCMMSC 2022, Hefei, China, December 15-18, 2022, Proceedings
E-Book
05/2023
Springer
€85.59
Available for download
Content
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 Veri?cation.- 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.