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Titolo: | Man-Machine Speech Communication : 17th National Conference, NCMMSC 2022, Hefei, China, December 15–18, 2022, Proceedings / / edited by Ling Zhenhua, Gao Jianqing, Yu Kai, Jia Jia |
Pubblicazione: | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
Edizione: | 1st ed. 2023. |
Descrizione fisica: | 1 online resource (342 pages) |
Disciplina: | 006.4 |
Soggetto topico: | Computer vision |
Natural language processing (Computer science) | |
Signal processing | |
Artificial intelligence | |
User interfaces (Computer systems) | |
Human-computer interaction | |
Computer Vision | |
Natural Language Processing (NLP) | |
Signal, Speech and Image Processing | |
Artificial Intelligence | |
User Interfaces and Human Computer Interaction | |
Soggetto non controllato: | Science |
Persona (resp. second.): | ZhenhuaLing |
Nota di bibliografia: | Includes bibliographical references and index. |
Nota di contenuto: | 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. |
Sommario/riassunto: | 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. |
Titolo autorizzato: | Man-Machine Speech Communication |
ISBN: | 981-9924-01-4 |
Formato: | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 9910720086103321 |
Lo trovi qui: | Univ. Federico II |
Opac: | Controlla la disponibilità qui |