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MAGIC DUST FOR CROSS-LINGUAL ADAPTATION OF MONOLINGUAL WAV2VEC-2.0
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In: ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03544515 ; ICASSP 2022, May 2022, Singapour, Singapore (2022)
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Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0 ...
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A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning
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In: Interspeech 2020 ; https://hal.archives-ouvertes.fr/hal-02912029 ; Interspeech 2020, Oct 2020, Shanghai, China (2020)
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CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning ...
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A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning ...
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The SUMMA Platform Prototype
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In: http://infoscience.epfl.ch/record/233575 (2017)
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Multi-view Dimensionality Reduction for Dialect Identification of Arabic Broadcast Speech ...
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Automatic Dialect Detection in Arabic Broadcast Speech ...
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Abstract:
We investigate different approaches for dialect identification in Arabic broadcast speech, using phonetic, lexical features obtained from a speech recognition system, and acoustic features using the i-vector framework. We studied both generative and discriminate classifiers, and we combined these features using a multi-class Support Vector Machine (SVM). We validated our results on an Arabic/English language identification task, with an accuracy of 100%. We used these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%. We further report results using the proposed method to discriminate between the five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine, North African, and MSA, with an accuracy of 52%. We discuss dialect identification errors in the context of dialect code-switching between Dialectal Arabic and MSA, and compare the error pattern between manually labeled data, and the output from our classifier. ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1509.06928 https://dx.doi.org/10.48550/arxiv.1509.06928
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