Towards phoneme inventory discovery for documentation of unwritten languages

Abstract

Documenting unwritten languages is a challenging task, even for trained specialists. To help linguists in better and faster documenting new languages is the goal of the French-German ANR-DFG project BULB. To discover the phonetic inventory of a language the project follows three steps: estimating phoneme boundaries, classifying articulatory features (AFs) for each individual segment and clustering the segments into a phoneme inventory. In this work, we focus on estimating the phoneme boundaries and the extraction of AFs, but also perform a first simple clustering based on the recognized AFs. We demonstrate that our Deep Bidirectional LSTM-based approach for identifying phoneme boundaries achieves state-of-the-art performance and evaluate AF extraction based on feed forward neural networks.