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This paper presents a state-of-the-art model for transcribing speech in anylanguage into the International Phonetic Alphabet (IPA). Transcription ofspoken languages into IPA is an essential yet time-consuming process inlanguage documentation, and even partially automating this process has thepotential to drastically speed up the documentation of endangered languages.Like the previous best speech-to-IPA model (Wav2Vec2Phoneme), our model isbased on wav2vec 2.0 and is fine-tuned to predict IPA from audio input. We usetraining data from seven languages from CommonVoice 11.0, transcribed into IPAsemi-automatically. Although this training dataset is much smaller thanWav2Vec2Phoneme's, its higher quality lets our model achieve comparable orbetter results. Furthermore, we show that the quality of our universalspeech-to-IPA models is close to that of human annotators.

Universal Automatic Phonetic Transcription into the International Phonetic Alphabet