centers in the United States generate more than a billion hours of
phone audio
every year, at a cost of over $10 billion. Despite this huge
investment, less than 25% of this audio is currently made
searchable or analyzed.  Other industries, such as legal discovery and
sales, also have large amounts of neglected but valuable audio content.

is a company launching out of our Winter 2016 class that uses deep
learning to index audio and make it searchable for businesses. For
example, a company can use DeepGram to analyze their phone support audio
dataset and search for moments where their competitors’ names are mentioned.

has been made possible thanks to the convergence of several relatively
recent technical developments: Cloud storage has enabled companies to
store their recorded audio cheaply; and advanced GPUs have allowed deep
learning to mature, so that it’s now possible to build deep neural
networks that can capture the complexity of speech.

search is also motivated by market factors. There has been a structural
change in phone support from on-site employees to a distributed
workforce which makes quality assurance more challenging. Businesses are
focusing more on data and analytics, and they want actionable insight
from their information-rich audio datasets.

status quo approach to the audio search problem is to use speech
recognition to create a transcript of the audio, and then use a
conventional text search to search the transcript. But the inevitable
errors in the computerized transcript make this work poorly. DeepGram is
far more accurate and efficient: For example, in the image above, DeepGram correctly identifies the word “turbine” where the speech transcript misreads it as “turban.”

DeepGram was founded by two particle
physicists searching for dark matter. The idea for the audio search
engine came from a weekend project of theirs, to record all the audio in a person’s
life 24/7. But with so much audio data, it was impossible to find and recall
certain moments, and they couldn’t find a search engine that
worked well with speech. So, they built an AI-based index and fuzzy search,
making their recorded audio accurately and easily searchable. They
realized the search engine would be even more useful for businesses with
large amounts of audio — and DeepGram was born.

Businesses can sign up and start using DeepGram now at