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E-raamat: Man-Machine Speech Communication: 17th National Conference, NCMMSC 2022, Hefei, China, December 15-18, 2022, Proceedings

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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.