Man-Machine Speech Communication [[electronic resource] ] : 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.: 996546844803316
• Lo trovi qui: Univ. di Salerno
• Serie: Communications in Computer and Information Science, . 1865-0937 ; ; 1765