Keyword spotting is a key feature of speech recognition software programs and tools. Speech recognition software relies on complex technologies to “understand” what someone is saying, and then convert it into text. In order to do this, the speech recognition software needs to rely on various technologies and analytical methods. One of these is keyword spotting.
Two different types of keyword spotting work differently. The first is keyword spotting in unconstrained speech, or the analysis of a linear stream of phonetics without specified word breaks. The other form is known as keyword spotting in isolated word recognition, where the software may have “clues” in terms of silence or breaks between words.
Keyword spotting in unconstrained speech relies on some specific programs called algorithms. These programs basically work with the “bits” or individual phonemes to predict what they most likely “mean,” or what context they are most likely to be placed in. One popular algorithm for this task is called iterative Viterbi encoding, which is sometimes explained as finding the “smallest normalized distance” of one sequence from another, in other words, comparing bits of data for “matching” that aids in speech recognition. Some of these algorithms are extremely effective in interpreting human speech without really understanding it in a sentient way.
The other type, keyword spotting in isolated word recognition, sometimes uses what experts call “dynamic time warping.” This process analyzes speed or pace in order to aid in speech recognition. There are a lot of analytical comparisons that help to fashion a final result, which interprets the words uniquely.
Both kinds of keyword spot strategies are sometimes explained by what pros call “hidden Markov models.” The Markov model is named for the scientist who came up with it, and uses complex statistical methods to find elusive results. Keyword spotting and other speech recognition software is based largely on probability, as well as recording of sequences and comparisons, so that the machine can generate text that more closely mirrors what is being said by the human user.
Speech to text technology is proving immensely useful for converting verbal communication to the page without the need for vast amounts of manual typing. It’s likely that keyword tools and other technologies will continue to drive ever more powerful speech recognition programs that will make communications more effective across different mediums. Technologies like these that go hand in hand with the digital transfer of information, which will bring more diverse abilities to the modern world and its citizens.
