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A vast majority of the world's 7,000 spoken languages are predicted to becomeextinct within this century, including the endangered language of Ladin fromthe Italian Alps. Linguists who work to preserve a language's phonetic andphonological structure can spend hours transcribing each minute of speech fromnative speakers. To address this problem in the context of Ladin, our paperpresents the first analysis of speech representations and machine learningmodels for classifying 32 phonemes of Ladin. We experimented with a noveldataset of the Fascian dialect of Ladin, collected from native speakers inItaly. We created frame-level and segment-level speech feature extractionapproaches and conducted extensive experiments with 8 different classifierstrained on 9 different speech representations. Our speech representationsranged from traditional features (MFCC, LPC) to features learned with deepneural network models (autoencoders, LSTM autoencoders, and WaveNet). Ourhighest-performing classifier, trained on MFCC representations of speechsignals, achieved an 86% average accuracy across all Ladin phonemes. We alsoobtained average accuracies above 77% for all Ladin phoneme subgroups examined.Our findings contribute insights for learning discriminative Ladin phonemerepresentations and demonstrate the potential for leveraging machine learningand speech signal processing to preserve Ladin and other endangered languages.

Speech Representations and Phoneme Classification for Preserving the Endangered Language of Ladin