In this paper, we introduce a massively multilingual speech corpora with
fine-grained phonemic transcriptions, encompassing more than 115 languages from
diverse language families. Based on this multilingual dataset, we propose
CLAP-IPA, a multilingual phoneme-speech contrastive embedding model capable of
open-vocabulary matching between speech signals and phonemically transcribed
keywords or arbitrary phrases. The proposed model has been tested on two
fieldwork speech corpora in 97 unseen languages, exhibiting strong
generalizability across languages. Comparison with a text-based model shows
that using phonemes as modeling units enables much better crosslinguistic
generalization than orthographic texts.Comment: Preprint; Work in Progres