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Titlebook: Man-Machine Speech Communication; 18th National Confer Jia Jia,Zhenhua Ling,Zixing Zhang Conference proceedings 2024 The Editor(s) (if appl

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發(fā)表于 2025-3-21 17:14:23 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Man-Machine Speech Communication
副標(biāo)題18th National Confer
編輯Jia Jia,Zhenhua Ling,Zixing Zhang
視頻videohttp://file.papertrans.cn/623/622082/622082.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Man-Machine Speech Communication; 18th National Confer Jia Jia,Zhenhua Ling,Zixing Zhang Conference proceedings 2024 The Editor(s) (if appl
描述This book constitutes the refereed proceedings of the 18th?National Conference on Man-Machine Speech Communication, NCMMSC 2023, held in?Suzhou, China, during?December 8–11, 2023..The? 20 full papers and 11 short papers included in this book were carefully reviewed and?selected from?117?submissions. They deal with topics such as speech recognition, synthesis, enhancement and coding, audio/music/singing synthesis, avatar, speaker recognition and verification, human–computer dialogue systems, large language models as well as phonetic and?linguistic topics such as speech prosody analysis, pathological speech analysis, experimental?phonetics, acoustic scene classification..
出版日期Conference proceedings 2024
關(guān)鍵詞speech processing; speech communication; computational liguistics; speech perception; speech recognition
版次1
doihttps://doi.org/10.1007/978-981-97-0601-3
isbn_softcover978-981-97-0600-6
isbn_ebook978-981-97-0601-3Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Jianquan Zhou,Yi Gao,Siyu Zhanglt of a careful research and extensive translation operation ensuring The alphabetical index of organisations throughout the entries are as accurate and up-to-date as possible. Eastern Europe and the c.rs. lists all entries in The Editors would like to express thanks to the huge alphabetical order i
板凳
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地板
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,Semi-End-to-End Nested Named Entity Recognition from?Speech,se a span classifier to classify only the spans that start with the predicted heads in transcriptions. From the experimental results on the nested NER dataset of Chinese speech CNERTA, our semi-E2E approach gets the best .1 score (1.84% and 0.53% absolute points higher than E2E and pipeline respecti
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發(fā)表于 2025-3-22 08:54:38 | 只看該作者
,APNet2: High-Quality and?High-Efficiency Neural Vocoder with?Direct Prediction of?Amplitude and?Phantroduce a multi-resolution discriminator (MRD) into the GAN-based losses and optimize the form of certain losses. At a common configuration with a waveform sampling rate of 22.05 kHz and spectral frame shift of 256 points (i.e., approximately 11.6 ms), our proposed APNet2 vocoder outperforms the or
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發(fā)表于 2025-3-22 13:58:18 | 只看該作者
,A Fast Sampling Method in?Diffusion-Based Dance Generation Models,uences during the iteration process, and eventually concatenating multiple short sequences to form a longer one. Experimental results show that our improved sampling method not only makes the generation speed faster, but also maintains the quality of the dance movements.
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Emotional Support Dialog System Through Recursive Interactions Among Large Language Models,tional support strategy, while the latter boasts strong reasoning capabilities and world knowledge. By interacting, our framework synergistically leverages the strengths of both models. Furthermore, we have integrated recursive units to maintain the continuity of dialogue strategy, working toward th
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發(fā)表于 2025-3-23 05:11:36 | 只看該作者
,Task-Adaptive Generative Adversarial Network Based Speech Dereverberation for?Robust Speech Recognie generator as a dereverberation system. By doing so, the corresponding output distribution will be more suitable for the recognition task. Experimental results on the REVERB corpus show that our proposed approach achieves a relative 18.6% and 8.6% word error rate reduction than the traditional GAN-
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發(fā)表于 2025-3-23 07:44:38 | 只看該作者
,A Framework Combining Separate and?Joint Training for?Neural Vocoder-Based Monaural Speech Enhancemhigh-fidelity, high-generation speed vocoder, which synthesizes the improved speech waveform. Following the pre-training of these two modules, they are stacked for joint training. Experimental results show the superiority of this approach in terms of speech quality, surpassing the performance of con
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