  The first step in any voice recognition software is to determine what
language a speaker is using, and ideally this process would be automated. The
technique described in this paper, language identification for audio
spectrograms (LIFAS), uses spectrograms generated from audio signals as inputs
to a convolutional neural network (CNN) to be used for language identification.
LIFAS requires minimal pre-processing on the audio signals as the spectrograms
are generated during each batch as they are input to the network during
training.
  LIFAS utilizes deep learning tools that are shown to be successful on image
processing tasks and applies it to audio signal classification. LIFAS performs
binary language classification with an accuracy of 97\%, and multi-class
classification with six languages at an accuracy of 89\% on 3.75 second audio
clips.
