Evaluation of WhisperIPA and ZIPA reveals persistent performance gaps across languages, accents, gender, ethnicity, and age even after allowing for similar phoneme substitutions.
The taste of IPA : Towards open-vocabulary keyword spotting and forced alignment in any language
2 Pith papers cite this work. Polarity classification is still indexing.
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Multilingual generative meta-learning for spoken word classification shows small gains over monolingual models, with unique data volume mattering more than the number of languages.
citing papers explorer
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Evaluating Bias in Phoneme-Based Automatic Speech Recognition Systems: An Analysis of IPA Transcription Models
Evaluation of WhisperIPA and ZIPA reveals persistent performance gaps across languages, accents, gender, ethnicity, and age even after allowing for similar phoneme substitutions.
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Does language matter for spoken word classification? A multilingual generative meta-learning approach
Multilingual generative meta-learning for spoken word classification shows small gains over monolingual models, with unique data volume mattering more than the number of languages.