Fadi Biadsy


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Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech
Katrin Tomanek | Vicky Zayats | Dirk Padfield | Kara Vaillancourt | Fadi Biadsy
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Automatic Speech Recognition (ASR) systems are often optimized to work best for speakers with canonical speech patterns. Unfortunately, these systems perform poorly when tested on atypical speech and heavily accented speech. It has previously been shown that personalization through model fine-tuning substantially improves performance. However, maintaining such large models per speaker is costly and difficult to scale. We show that by adding a relatively small number of extra parameters to the encoder layers via so-called residual adapter, we can achieve similar adaptation gains compared to model fine-tuning, while only updating a tiny fraction (less than 0.5%) of the model parameters. We demonstrate this on two speech adaptation tasks (atypical and accented speech) and for two state-of-the-art ASR architectures.


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Improving the Arabic Pronunciation Dictionary for Phone and Word Recognition with Linguistically-Based Pronunciation Rules
Fadi Biadsy | Nizar Habash | Julia Hirschberg
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Spoken Arabic Dialect Identification Using Phonotactic Modeling
Fadi Biadsy | Julia Hirschberg | Nizar Habash
Proceedings of the EACL 2009 Workshop on Computational Approaches to Semitic Languages

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Contextual Phrase-Level Polarity Analysis Using Lexical Affect Scoring and Syntactic N-Grams
Apoorv Agarwal | Fadi Biadsy | Kathleen R. McKeown
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)


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An Unsupervised Approach to Biography Production Using Wikipedia
Fadi Biadsy | Julia Hirschberg | Elena Filatova
Proceedings of ACL-08: HLT