LEADER 05527nam 22007335 450 001 9910720086103321 005 20230509085529.0 010 $a981-9924-01-4 024 7 $a10.1007/978-981-99-2401-1 035 $a(CKB)5720000000183818 035 $a(MiAaPQ)EBC7248740 035 $a(Au-PeEL)EBL7248740 035 $a(DE-He213)978-981-99-2401-1 035 $a(BIP)090181598 035 $a(PPN)270613749 035 $a(EXLCZ)995720000000183818 100 $a20230509d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMan-Machine Speech Communication $e17th National Conference, NCMMSC 2022, Hefei, China, December 15?18, 2022, Proceedings /$fedited by Ling Zhenhua, Gao Jianqing, Yu Kai, Jia Jia 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (342 pages) 225 1 $aCommunications in Computer and Information Science,$x1865-0937 ;$v1765 311 $a981-9924-00-6 320 $aIncludes bibliographical references and index. 327 $aMCPN: 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. 330 $aThis book constitutes the refereed proceedings of the 17th National Conference on Man?Machine Speech Communication, NCMMSC 2022, held in China, in December 2022. The 21 full papers and 7 short papers included in this book were carefully reviewed and selected from 108 submissions. They were organized in topical sections as follows: MCPN: A Multiple Cross-Perception Network for Real-Time Emotion Recognition in Conversation.- Baby Cry Recognition Based on Acoustic Segment Model, MnTTS2 An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis Dataset. 410 0$aCommunications in Computer and Information Science,$x1865-0937 ;$v1765 606 $aComputer vision 606 $aNatural language processing (Computer science) 606 $aSignal processing 606 $aArtificial intelligence 606 $aUser interfaces (Computer systems) 606 $aHuman-computer interaction 606 $aComputer Vision 606 $aNatural Language Processing (NLP) 606 $aSignal, Speech and Image Processing 606 $aArtificial Intelligence 606 $aUser Interfaces and Human Computer Interaction 610 $aScience 615 0$aComputer vision. 615 0$aNatural language processing (Computer science). 615 0$aSignal processing. 615 0$aArtificial intelligence. 615 0$aUser interfaces (Computer systems). 615 0$aHuman-computer interaction. 615 14$aComputer Vision. 615 24$aNatural Language Processing (NLP). 615 24$aSignal, Speech and Image Processing . 615 24$aArtificial Intelligence. 615 24$aUser Interfaces and Human Computer Interaction. 676 $a006.4 702 $aZhenhua$b Ling 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910720086103321 996 $aMan-Machine Speech Communication$93389332 997 $aUNINA