Framework for cross-language automatic phonetic segmentation

Annotation of large multilingual corpora remains a challenge to the data-driven approach to speech research, especially for under-resourced languages. This paper presents cross-language automatic phonetic segmentation using Hidden Markov Models (HMMs). The underlying notion is segmentation based on articulation (manner and place) so as to provide extensive models that will be applicable across languages. A test on the Appen Spanish speech corpus gives phone recognition accuracy of 61.15% when bootstrapped with acoustic models trained on the TIMIT as compared with a baseline result of 54.63% for flat start initialization of the monophone models.