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Keyword Spotting
Keyword spotting (or more simply, word 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 (also called hot word) detection used by personal digital assistants such as Alexa or Siri to activate the dormant speaker, in other words "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 volu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] [Amazon] |
Speech Processing
Speech processing is the study of speech signals and the processing methods of signals. The signals are usually processed in a digital representation, so speech processing can be regarded as a special case of digital signal processing, applied to speech signals. Aspects of speech processing includes the acquisition, manipulation, storage, transfer and output of speech signals. Different speech processing tasks include speech recognition, speech synthesis, speaker diarization, speech enhancement, speaker recognition, etc. History Early attempts at speech processing and recognition were primarily focused on understanding a handful of simple phonetic elements such as vowels. In 1952, three researchers at Bell Labs, Stephen. Balashek, R. Biddulph, and K. H. Davis, developed a system that could recognize digits spoken by a single speaker. Pioneering works in field of speech recognition using analysis of its spectrum were reported in the 1940s. Linear predictive coding (LPC), a sp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] [Amazon] |
Keyword (linguistics)
In corpus linguistics a key word is a word which occurs in a text more often than we would expect to occur by chance alone. Key words are calculated by carrying out a statistical test (e.g., loglinear or chi-squared) which compares the word frequencies in a text against their expected frequencies derived in a much larger corpus, which acts as a reference for general language use. Keyness is then the quality a word or phrase has of being "key" in its context. Combinations of nouns with parts of speech that human readers would not likely notice, such as prepositions, time adverbs, and pronouns can be a relevant part of keyness. Even separate pronouns can constitute keywords. Compare this with collocation, the quality linking two words or phrases usually assumed to be within a given span of each other. Keyness is a ''textual'' feature, not a language feature (so a word has keyness in a certain textual context but may well not have keyness in other contexts, whereas a node and colloca ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] [Amazon] |
Utterance
In spoken language analysis, an utterance is a continuous piece of speech, by one person, before or after which there is silence on the part of the person. In the case of oral language, spoken languages, it is generally, but not always, bounded by silence. In written language, utterances only exist indirectly, though their representations or portrayals. They can be represented and delineated in written language in many ways. In spoken language, utterances have several characteristics such as paralinguistic features, which are aspects of speech such as facial expression, gesture, and posture. Prosody (linguistics) , Prosodic features include Stress (linguistics), stress, Intonation (linguistics), intonation, and Paralanguage, tone of voice, as well as ellipsis, which are words that the listener inserts in spoken language to fill gaps. Moreover, other aspects of utterances found in spoken languages are non-fluency features including: voiced/un-voiced pauses (i.e. "umm"), tag quest ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] [Amazon] |
