A Hebrew G2P system that outputs fully specified IPA with stress, along with a new IPA-annotated speech corpus, improves phonetic accuracy of small real-time TTS models.
ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development
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abstract
We introduce "ivrit.ai", a comprehensive Hebrew speech dataset, addressing the distinct lack of extensive, high-quality resources for advancing Automated Speech Recognition (ASR) technology in Hebrew. With over 3,300 speech hours and a over a thousand diverse speakers, ivrit.ai offers a substantial compilation of Hebrew speech across various contexts. It is delivered in three forms to cater to varying research needs: raw unprocessed audio; data post-Voice Activity Detection, and partially transcribed data. The dataset stands out for its legal accessibility, permitting use at no cost, thereby serving as a crucial resource for researchers, developers, and commercial entities. ivrit.ai opens up numerous applications, offering vast potential to enhance AI capabilities in Hebrew. Future efforts aim to expand ivrit.ai further, thereby advancing Hebrew's standing in AI research and technology.
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Phonikud: Overcoming Phonetic Underspecification for Hebrew Text-To-Speech
A Hebrew G2P system that outputs fully specified IPA with stress, along with a new IPA-annotated speech corpus, improves phonetic accuracy of small real-time TTS models.