Word Spotting

Word spotting has been an active area of speech recognition for over twenty years. Although it initially addressed applications requiring the scanning of audio data for occurrences of particular keywords, the technology has become an effective approach to speech recognition for a wide range of applications. The term “word spotting” is now used to refer to a variety of techniques that are useful in speech recognition applications where relevant information, such as a command, must be recognized even when it is embedded in irrelevant speech input or other audio interference, or when the desired information may not be present. The related areas of filler modeling and out-of-set rejection share many of the same underlying technical problems and approaches to word spotting. Depending on the particular application, different types and combinations of word spotting techniques are appropriate and effective. Most recently, a variety of statistical modeling techniques have provided higher accuracy than previous approaches. Many of these techniques share aspects, such as use of hidden Markov models (HMMs) and statistical language models, with other areas of speech recognition. This chapter presents a survey of various approaches to word spotting and related areas, suggests appropriate applications of these approaches, and identifies unresolved research problems.