  Automatic spoken language identification (LID) is a very important research
field in the era of multilingual voice-command-based human-computer interaction
(HCI). A front-end LID module helps to improve the performance of many
speech-based applications in the multilingual scenario. India is a populous
country with diverse cultures and languages. The majority of the Indian
population needs to use their respective native languages for verbal
interaction with machines. Therefore, the development of efficient Indian
spoken language recognition systems is useful for adapting smart technologies
in every section of Indian society. The field of Indian LID has started gaining
momentum in the last two decades, mainly due to the development of several
standard multilingual speech corpora for the Indian languages. Even though
significant research progress has already been made in this field, to the best
of our knowledge, there are not many attempts to analytically review them
collectively. In this work, we have conducted one of the very first attempts to
present a comprehensive review of the Indian spoken language recognition
research field. In-depth analysis has been presented to emphasize the unique
challenges of low-resource and mutual influences for developing LID systems in
the Indian contexts. Several essential aspects of the Indian LID research, such
as the detailed description of the available speech corpora, the major research
contributions, including the earlier attempts based on statistical modeling to
the recent approaches based on different neural network architectures, and the
future research trends are discussed. This review work will help assess the
state of the present Indian LID research by any active researcher or any
research enthusiasts from related fields.
