  While most modern speech Language Identification methods are closed-set, we
want to see if they can be modified and adapted for the open-set problem. When
switching to the open-set problem, the solution gains the ability to reject an
audio input when it fails to match any of our known language options. We tackle
the open-set task by adapting two modern-day state-of-the-art approaches to
closed-set language identification: the first using a CRNN with attention and
the second using a TDNN. In addition to enhancing our input feature embeddings
using MFCCs, log spectral features, and pitch, we will be attempting two
approaches to out-of-set language detection: one using thresholds, and the
other essentially performing a verification task. We will compare both the
performance of the TDNN and the CRNN, as well as our detection approaches.
