ISCA - International Speech |
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ISCA Best Paper Awards - 2024We would like to highlight the award-winning papers! Each year ISCA awards 3 best student papers at Interspeech based on anonymous reviewing and presentation at the conference. The Interspeech Area Chairs nominate candidate papers that are assessed by a jury with representatives from the ISCA Board, Area Chairs and the Interspeech Technical Program Chairs. The jury for the best student paper award is impartial, i.e. members cannot participate in the voting if (s)he is in any way involved in/with any of the award candidate. Each paper is awarded 500 euros to be split between the student authors. Best Papers of the journals Speech Communication, and Computer Speech and Language are also announced by ISCA during Interspeech. Please see best paper awards going back to 2000 here. |
ISCA Award for Best Student Paper (students in bold)Analysis of articulatory setting for L1 and L2 English speakers using MRI data Kevin Huang, Jack Goldberg, Louis Goldstein and Shrikanth Narayanan [pdf]
A Contrastive Learning Approach to Mitigate Bias in Speech Models Alkis Koudounas, Flavio Giobergia, Eliana Pastor and Elena Baralis [pdf]
SimpleSpeech: Towards Simple and Efficient Text-to-Speech with Scalar Latent Transformer Diffusion Models Dongchao Yang, Dingdong Wang, Haohan Guo, Xueyuan Chen, Xixin Wu and Helen Meng [pdf]
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Identifying Mild Cognitive Impairment and mild Alzheimer’s disease based on spontaneous speech using ASR and linguistic features
Gábor Gosztolya, Veronika Vincze, László Tóth, Magdolna Pákáski, János Kálmán, Ildikó Hoffmann, Computer Speech & Language, Volume 53, Pages 181-197, January 2019 [link]
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Abstract: Alzheimer’s disease (AD) is a neurodegenerative disorder that develops for years before clinical manifestation, while mild cognitive impairment is clinically considered as a prodromal stage of AD. For both types of neurodegenerative disorders, early diagnosis is crucial for the timely treatment and to decelerate progression. Unfortunately, the current diagnostic solutions are time-consuming. Here, we seek to exploit the observation that these illnesses frequently disturb the mental and linguistic functions, which might be detected from the spontaneous speech produced by the patient. First, we present an automatic speech recognition based procedure for the extraction of a special set of acoustic features. Second, we present a linguistic feature set that is extracted from the transcripts of the same speech signals. The usefulness of the two feature sets is evaluated via machine learning experiments, where our goal is not only to differentiate between the patients and the healthy control group, but also to tell apart Alzheimer’s patients from those with mild cognitive impairment. Our results show that based on only the acoustic features, we are able to separate the various groups with accuracy scores between 74–82%. We attained similar accuracy scores when using only the linguistic features. With the combination of the two types of features, the accuracy scores rise to between 80–86%, and the corresponding F1 values also fall between 78–86%. We hope that with the full automation of the processing chain, our method can serve as the basis of an automatic screening test in the future. |
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ISCA Award for the Best Paper published in Speech Communication (2019-2023)CN-Celeb: Multi-genre speaker recognition Lantian Li, Ruiqi Liu, Jiawen Kang, Yue Fan, Hao Cui, Yunqi Cai, Ravichander Vipperla, Thomas Fang Zheng, Dong Wang, Speech Communication, Volume 137, Pages 77-91, February 2022 [link]
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