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

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

11.05.2023

Abbildungen

XI, 332 p. 91 illus., 86 illus. in color.

Herausgeber

Ling Zhenhua + weitere

Verlag

Springer Singapore

Seitenzahl

332

Maße (L/B/H)

23,5/15,5/1,9 cm

Gewicht

522 g

Auflage

1st ed. 2023

Sprache

Englisch

ISBN

978-981-9924-00-4

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

11.05.2023

Abbildungen

XI, 332 p. 91 illus., 86 illus. in color.

Herausgeber

Verlag

Springer Singapore

Seitenzahl

332

Maße (L/B/H)

23,5/15,5/1,9 cm

Gewicht

522 g

Auflage

1st ed. 2023

Sprache

Englisch

ISBN

978-981-9924-00-4

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Man-Machine Speech Communication
  • Produktbild: Man-Machine Speech Communication
  • 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.