  Most state-of-the-art spoken language identification models are closed-set;
in other words, they can only output a language label from the set of classes
they were trained on. Open-set spoken language identification systems, however,
gain the ability to detect when an input exhibits none of the original
languages. In this paper, we implement a novel approach to open-set spoken
language identification that uses MFCC and pitch features, a TDNN model to
extract meaningful feature embeddings, confidence thresholding on softmax
outputs, and LDA and pLDA for learning to classify new unknown languages. We
present a spoken language identification system that achieves 91.76% accuracy
on trained languages and has the capability to adapt to unknown languages on
the fly. To that end, we also built the CU MultiLang Dataset, a large and
diverse multilingual speech corpus which was used to train and evaluate our
system.

Computation and Language;Artificial Intelligence;Machine Learning;Audio and Speech Processing