  Due to a drastic improvement in the quality of internet services worldwide,
there is an explosion of multilingual content generation and consumption. This
is especially prevalent in countries with large multilingual audience, who are
increasingly consuming media outside their linguistic familiarity/preference.
Hence, there is an increasing need for real-time and fine-grained content
analysis services, including language identification, content transcription,
and analysis. Accurate and fine-grained spoken language detection is an
essential first step for all the subsequent content analysis algorithms.
Current techniques in spoken language detection may lack on one of these
fronts: accuracy, fine-grained detection, data requirements, manual effort in
data collection \& pre-processing. Hence in this work, a real-time language
detection approach to detect spoken language from 5 seconds' audio clips with
an accuracy of 91.8\% is presented with exiguous data requirements and minimal
pre-processing. Novel architectures for Capsule Networks is proposed which
operates on spectrogram images of the provided audio snippets. We use previous
approaches based on Recurrent Neural Networks and iVectors to present the
results. Finally we show a ``Non-Class'' analysis to further stress on why
CapsNet architecture works for LID task.
