  A vast majority of the world's 7,000 spoken languages are predicted to become
extinct within this century, including the endangered language of Ladin from
the Italian Alps. Linguists who work to preserve a language's phonetic and
phonological structure can spend hours transcribing each minute of speech from
native speakers. To address this problem in the context of Ladin, our paper
presents the first analysis of speech representations and machine learning
models for classifying 32 phonemes of Ladin. We experimented with a novel
dataset of the Fascian dialect of Ladin, collected from native speakers in
Italy. We created frame-level and segment-level speech feature extraction
approaches and conducted extensive experiments with 8 different classifiers
trained on 9 different speech representations. Our speech representations
ranged from traditional features (MFCC, LPC) to features learned with deep
neural network models (autoencoders, LSTM autoencoders, and WaveNet). Our
highest-performing classifier, trained on MFCC representations of speech
signals, achieved an 86% average accuracy across all Ladin phonemes. We also
obtained average accuracies above 77% for all Ladin phoneme subgroups examined.
Our findings contribute insights for learning discriminative Ladin phoneme
representations and demonstrate the potential for leveraging machine learning
and speech signal processing to preserve Ladin and other endangered languages.
