Keyword spotting


Keyword spotting is a problem that was historically first defined in the context of speech processing.
In speech processing, keyword spotting deals with the identification of keywords in utterances.
Keyword spotting is also defined as a separate, but related, problem in the context of document image processing.
In document image processing, keyword spotting is the problem of finding all instances of a query word that exist in a scanned document image, without fully recognizing it.

In speech processing

The first works in keyword spotting appeared in the late 1980s.
A special case of keyword spotting is wake word detection used by personal digital assistants such as Alexa or Siri to "wake up" when their name is spoken.
In the United States, the National Security Agency has made use of keyword spotting since at least 2006. This technology allows analysts to search through large volumes of recorded conversations and isolate mentions of suspicious keywords. Recordings can be indexed and analysts can run queries over the database to find conversations of interest. IARPA funded research into keyword spotting in the Babel program.
Some algorithms used for this task are:
Keyword spotting in document image processing can be seen as an instance of the more generic problem of content-based image retrieval.
Given a query, the goal is to retrieve the most relevant instances of words in a collection of scanned documents.
The query may be a text string or a word image.