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1
Code-Switching Text Augmentation for Multilingual Speech Processing ...
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2
What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition ...
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3
Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition ...
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4
Speaker detection in the wild: Lessons learned from JSALT 2019
In: Odyssey 2020 The Speaker and Language Recognition Workshop ; https://hal.archives-ouvertes.fr/hal-02417632 ; Odyssey 2020 The Speaker and Language Recognition Workshop, Nov 2020, Tokyo, Japan (2020)
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5
Learning Speaker Embedding from Text-to-Speech ...
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6
How Phonotactics Affect Multilingual and Zero-shot ASR Performance ...
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7
Self-Expressing Autoencoders for Unsupervised Spoken Term Discovery ...
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8
That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages ...
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9
Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson’s Disease
Abstract: Literature documents the impact of Parkinson’s Disease (PD) on speech but no study has analyzed in detail the importance of the distinct phonemic groups for the automatic identification of the disease. This study presents new approaches that are evaluated in three different corpora containing speakers suffering from PD with two main objectives: to investigate the influence of the different phonemic groups in the detection of PD and to propose more accurate detection schemes employing speech. The proposed methodology uses GMM-UBM classifiers combined with a technique introduced in this paper called phonemic grouping, that permits observation of the differences in accuracy depending on the manner of articulation. Cross-validation results reach accuracies between 85% and 94% with AUC ranging from 0.91 to 0.98, while cross-corpora trials yield accuracies between 75% and 82% with AUC between 0.84 and 0.95, depending on the corpus. This is the first work analyzing the generalization properties of the proposed approaches employing cross-corpora trials and reaching high accuracies. Among the different phonemic groups, results suggest that plosives, vowels and fricatives are the most relevant acoustic segments for the detection of PD with the proposed schemes. In addition, the use of text-dependent utterances leads to more consistent and accurate models.
Keyword: Article
URL: https://doi.org/10.1038/s41598-019-55271-y
http://www.ncbi.nlm.nih.gov/pubmed/31836744
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6910953/
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10
Language model integration based on memory control for sequence to sequence speech recognition ...
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11
Low-Resource Contextual Topic Identification on Speech ...
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12
An Empirical Evaluation of Zero Resource Acoustic Unit Discovery ...
Liu, Chunxi; Yang, Jinyi; Sun, Ming. - : arXiv, 2017
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13
NeuroSpeech: An open-source software for Parkinson's speech analysis
In: http://infoscience.epfl.ch/record/230231 (2017)
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14
The MITLL NIST LRE 2015 Language Recognition System
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15
Automatic Dialect Detection in Arabic Broadcast Speech ...
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16
Front-end factor analysis for speaker verification
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 19 (2011) 4, 788-798
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17
A study of interspeaker variability in speaker verification
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 16 (2008) 5, 980-988
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18
Modeling prosodic features with joint factor analysis for speaker verification
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 15 (2007) 7, 2095-2103
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OLC Linguistik
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