Papers by Hawau Toyin

2 papers
STTATTS: Unified Speech-To-Text And Text-To-Speech Model (2024.findings-emnlp)

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Challenge: a multi-task learning approach is currently available for speech recognition and speech synthesis models .
Approach: They propose a parameter-efficient approach to learning ASR and TTS jointly . they use English as a resource-rich language and Arabic as 'low-resource' language .
Outcome: The proposed model saves 50% of computational and memory costs while learning ASR and TTS jointly.
PolyWER: A Holistic Evaluation Framework for Code-Switched Speech Recognition (2024.findings-emnlp)

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Challenge: Existing methods for measuring accuracy, such as Word Error Rate (WER), are too strict to address this challenge.
Approach: They propose a framework for evaluating speech recognition systems to handle language-mixing by appending annotations to a publicly available Arabic-English code-switched dataset.
Outcome: The proposed framework evaluates speech recognition systems against human judgement and a publicly available Arabic-English code-switched dataset.

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