  The past decade has witnessed a groundbreaking rise of machine learning for
human language analysis, with current methods capable of automatically
accurately recovering various aspects of syntax and semantics - including
sentence structure and grounded word meaning - from large data collections.
Recent research showed the promise of such tools for analyzing acoustic
communication in nonhuman species. We posit that machine learning will be the
cornerstone of future collection, processing, and analysis of multimodal
streams of data in animal communication studies, including bioacoustic,
behavioral, biological, and environmental data. Cetaceans are unique non-human
model species as they possess sophisticated acoustic communications, but
utilize a very different encoding system that evolved in an aquatic rather than
terrestrial medium. Sperm whales, in particular, with their highly-developed
neuroanatomical features, cognitive abilities, social structures, and discrete
click-based encoding make for an excellent starting point for advanced machine
learning tools that can be applied to other animals in the future. This paper
details a roadmap toward this goal based on currently existing technology and
multidisciplinary scientific community effort. We outline the key elements
required for the collection and processing of massive bioacoustic data of sperm
whales, detecting their basic communication units and language-like
higher-level structures, and validating these models through interactive
playback experiments. The technological capabilities developed by such an
undertaking are likely to yield cross-applications and advancements in broader
communities investigating non-human communication and animal behavioral
research.
