Universal Automatic Phonetic Transcription into the International
  Phonetic Alphabet
Chihiro Taguchi Yusuke Sakai Parisa Haghani David Chiang
This paper presents a state-of-the-art model for transcribing speech in any
language into the International Phonetic Alphabet (IPA). Transcription of
spoken languages into IPA is an essential yet time-consuming process in
language documentation, and even partially automating this process has the
potential to drastically speed up the documentation of endangered languages.
Like the previous best speech-to-IPA model (Wav2Vec2Phoneme), our model is
based on wav2vec 2.0 and is fine-tuned to predict IPA from audio input. We use
training data from seven languages from CommonVoice 11.0, transcribed into IPA
semi-automatically. Although this training dataset is much smaller than
Wav2Vec2Phoneme's, its higher quality lets our model achieve comparable or
better results. Furthermore, we show that the quality of our universal
speech-to-IPA models is close to that of human annotators.