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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1...3 4 5 6 7 8 9 10 11...690 Hits 121 – 140 of 13.783 121 Emotion Intensity and its Control for Emotional Voice Conversion ... Zhou, Kun; Sisman, Berrak; Rana, Rajib. - : arXiv, 2022 BASE Show details 122 Suum Cuique: Studying Bias in Taboo Detection with a Community Perspective ... Khalid, Osama; Rusert, Jonathan; Srinivasan, Padmini. - : arXiv, 2022 BASE Show details 123 An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese Codex ... Lin, Chun-Hsien; Cheng, Pu-Jen. - : arXiv, 2022 BASE Show details 124 Application of Quantum Density Matrix in Classical Question Answering and Classical Image Classification ... Zhao, X. Q.; Wan, H.; Chen, H.. - : arXiv, 2022 BASE Show details 125 Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense ... Le, Thai; Lee, Jooyoung; Yen, Kevin. - : arXiv, 2022 BASE Show details 126 Detecting early signs of depression in the conversational domain: The role of transfer learning in low-resource scenarios ... Lorenc, Petr; Uban, Ana-Sabina; Rosso, Paolo. - : arXiv, 2022 BASE Show details 127 LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback ... Zhu, Yunchang; Pang, Liang; Lan, Yanyan. - : arXiv, 2022 BASE Show details 128 Counterfactual Explanations for Natural Language Interfaces ... Tolkachev, George; Mell, Stephen; Zdancewic, Steve. - : arXiv, 2022 BASE Show details 129 Uniform Complexity for Text Generation ... Imperial, Joseph Marvin. - : arXiv, 2022 BASE Show details 130 Probing Speech Emotion Recognition Transformers for Linguistic Knowledge ... Triantafyllopoulos, Andreas; Wagner, Johannes; Wierstorf, Hagen. - : arXiv, 2022 BASE Show details 131 GreaseLM: Graph REASoning Enhanced Language Models for Question Answering ... Zhang, Xikun; Bosselut, Antoine; Yasunaga, Michihiro. - : arXiv, 2022 BASE Show details 132 Position-based Prompting for Health Outcome Generation ... Abaho, M.; Bollegala, D.; Williamson, P.. - : arXiv, 2022 BASE Show details 133 Dilated Convolutional Neural Networks for Lightweight Diacritics Restoration ... Csanády, Bálint; Lukács, András. - : arXiv, 2022 BASE Show details 134 GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records ... Yang, Xi; PourNejatian, Nima; Shin, Hoo Chang. - : arXiv, 2022 BASE Show details 135 FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations ... Mei, Lingjie; Mao, Jiayuan; Wang, Ziqi. - : arXiv, 2022 BASE Show details 136 Low-dimensional representation of infant and adult vocalization acoustics ... Pagliarini, Silvia; Schneider, Sara; Kello, Christopher T.. - : arXiv, 2022 BASE Show details 137 Chain-based Discriminative Autoencoders for Speech Recognition ... Lee, Hung-Shin; Huang, Pin-Tuan; Cheng, Yao-Fei. - : arXiv, 2022 BASE Show details 138 Error Correction in ASR using Sequence-to-Sequence Models ... Dutta, Samrat; Jain, Shreyansh; Maheshwari, Ayush. - : arXiv, 2022 BASE Show details 139 Filter-based Discriminative Autoencoders for Children Speech Recognition ... Tai, Chiang-Lin; Lee, Hung-Shin; Tsao, Yu; Wang, Hsin-Min. - : arXiv, 2022 Abstract: Children speech recognition is indispensable but challenging due to the diversity of children's speech. In this paper, we propose a filter-based discriminative autoencoder for acoustic modeling. To filter out the influence of various speaker types and pitches, auxiliary information of the speaker and pitch features is input into the encoder together with the acoustic features to generate phonetic embeddings. In the training phase, the decoder uses the auxiliary information and the phonetic embedding extracted by the encoder to reconstruct the input acoustic features. The autoencoder is trained by simultaneously minimizing the ASR loss and feature reconstruction error. The framework can make the phonetic embedding purer, resulting in more accurate senone (triphone-state) scores. Evaluated on the test set of the CMU Kids corpus, our system achieves a 7.8% relative WER reduction compared to the baseline system. In the domain adaptation experiment, our system also outperforms the baseline system on the ... : Submitted to EUSIPCO 2022 ... Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD URL: https://arxiv.org/abs/2204.00164 https://dx.doi.org/10.48550/arxiv.2204.00164 BASE Hide details 140 Speaking clearly improves speech segmentation by statistical learning under optimal listening conditions ... Guo, Zhe-chen. - : Open Science Framework, 2022 BASE Show details Page: 1...3 4 5 6 7 8 9 10 11...690

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