Man-Machine Speech Communication : 17th National Conference, NCMMSC 2022, Hefei, China, December 15-18, 2022, Proceedings (Communications in Computer and Information Science)

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Man-Machine Speech Communication : 17th National Conference, NCMMSC 2022, Hefei, China, December 15-18, 2022, Proceedings (Communications in Computer and Information Science)

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  • 製本 Paperback:紙装版/ペーパーバック版／ページ数 335 p.
  • 言語 ENG
  • 商品コード 9789819924004

Full 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.

Contents

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 Verification.- 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.