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Speech and Computer: 26th International Conference, SPECOM 2024, Belgrade, Serbia, November 2528, 2024, Proceedings, Part II [Pehme köide]

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  • Formaat: Paperback / softback, 381 pages, kõrgus x laius: 235x155 mm, 106 Illustrations, color; 23 Illustrations, black and white; XVIII, 381 p. 129 illus., 106 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 15300
  • Ilmumisaeg: 22-Nov-2024
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031780132
  • ISBN-13: 9783031780134
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  • Formaat: Paperback / softback, 381 pages, kõrgus x laius: 235x155 mm, 106 Illustrations, color; 23 Illustrations, black and white; XVIII, 381 p. 129 illus., 106 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 15300
  • Ilmumisaeg: 22-Nov-2024
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031780132
  • ISBN-13: 9783031780134
The two-volume set LNAI 15299 and 15300 constitutes the refereed proceedings of the 26th International Conference on Speech and Computer, SPECOM 2024, held in Belgrade, Serbia, during November 2528, 2024.





The 53 full papers included in these proceedings were carefully reviewed and selected from 90 submissions. The book also contains two invited talks in full paper length. The papers are organized in the following topical sections:





Volume I:





Invited papers; automatic speech recognition; speech and language resources; speech synthesis and perception; and speech processing for medicine.





Volume II:





Computational paralinguistics; affective computing; speaker recognition; digital speech processing; natural language processing.
1 Computational Paralinguistics.- A Cross-Multi-Modal Fusion Approach
for Enhanced Engagement Recognition.- Automatic Assessment of Signs of
Alcohol Dependency Syndrome from Spontaneous Speech.- An Enhanced Compact
Convolution Transformer for Age, Gender and Emotion Detection in Egyptian
Arabic Speech.- RAG and Few-Shot Prompting in Emotional Text
Generation.- Sentiment Analysis for Egyptian Arabic-English Code-Switched
Data using Traditional Neural Models and Advanced Language Models.- Automatic
Detection of Irony Based on Acoustic Features and Facial
Expressions.- Affective Computing.- Emotion Recognition by Vocalizations of
Nonhuman Primates: Human and Automatic Classification.- MMHS: Multimodal
Model for Hate Speech Intensity Prediction.- Multimodal Emotion Recognition
using Compressed Graph Neural Networks.-Utilizing Speaker Models and Topic
Markers for Emotion Recognition in Dialogues.- How Children Recognize
Emotions from Video and Audio.- Speaker Recognition.- On the Influence of
CNN-based Feature Learning Modules in Neural Speaker Verification
Framework.- Voice Cloning and Mismatch Conditions in Forensic Automatic
Speaker Recognition.- Transformation of Emotional Speech to Anger Speech to
Reduce Mismatches in Testing and Enrollment Speech for Speaker Recognition
System.- Investigating Data Requirements for Hindi Speaker Recognition: A
Comparative Study with English.- Practical Evaluation and Validation of
Methods for Automatic Speaker Identification (as Applied to Various
Languages).- Digital Speech Processing.- In Pursuit for the Best Error Metric
for Optimisation of Articulatory Vowel Synthesis.- Exploring MetaConformer
for Speech Enhancement.- Integration of Short-Term and Long-Term Harmonic
Peaks in a Two-Level Discriminative Weight Training Framework for Voice
Activity Detection.- Separating Party Conversation by Applying Contrastive
Learning Methodology.- DuFCALF: Instilling Sentience in Computerized Song
Analysis.- Natural Language Processing.-Harnessing Knowledge Distillation for
Enhanced Text-to-Text Translation in Low-Resource Languages.- Bias Unveiled:
Enhancing Fairness in German Word Embeddings with Large Language
Models.- Conformer LLM - Convolution Augmented Large Language Models.- How to
Detect Imbalances in the Google Books Ngram Corpus?.- Predicting the Valence
Rating of Russian Words Using Various Pre-Trained Word Embeddings.- 3 Ancient
Egyptian Hieroglyphic Texts Structure Identification.