| Speech Segmentation |
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Information AboutSpeech Segmentation |
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| natural language processing | |
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Speech segmentation is an important subproblem of Speech Recognition , and cannot be adequately solved in isolation. As in most Natural Language Processing problems, one must take into account Context , Grammar , and Semantics , and even so the result is often a Probabilistic division rather than a categorical. PHONETIC SEGMENTATION The lowest level of speech segmentation is the breakup and classification of the sound signal into a string of phonemes. The difficulty of this problem is compounded by the phenomenon of Co-articulation of phonemes, where one phoneme may be modified in various ways by the adjacent sounds: it may blend smoothly with them, fuse with them, split, or even disappear. This phenomenon may happen between adjacent words as well as within a single word. Co-articulation is widespread in many languages, including English: for example, the sentence "what are you going to do?" when spoken might sound like "whatchagonnado?". Therefore, even with the best algorithms, the result of phonetic segmetnation will usually be very distant from the standard written language. For this reason, the lexical and syntactic parsing of spoken text normally requires specialized algorithms, distinct from those used for parsing written text. LEXICAL SEGMENTATION In all natural languages, the meaning of a complex spoken sentence (which often has never been heard or uttered before) can be understood only by decomposing it into smaller ''lexical segments'' (roughly, the Word s of the language), associating a meaning to each segment, and then combining those meanings according to the grammar rules of the language. The recognition of each lexical segment in turn requires its decomposition into a sequence of discrete ''phonetic segments'' and mapping each segment to one element of a finite set of elementary sounds (roughly, the Phoneme s of the language); the meaning then can be found by standard Table Lookup Algorithms . For most spoken languages, the boundaries between lexical units are surprisingly difficult to identify. One might expect that the inter-word spaces used by many written languages, like English or Spanish, would correspond to pauses in their spoken version; but that is true only in very slow speech, when the speaker deliberately inserts those pauses. In normal speech, one typically finds many consecutive words being said with no pauses between them, and often the final sounds of one word blend smoothly or fuse with the initial sounds of the next word. Moreover, an utterance can have different meanings depending on how it is split into words. A popular example, often quoted in the field, is the phrase ''How to wreck a nice beach'', which sounds very similar to ''How to recognize speech''. As this example shows, proper lexical segmetnation depends on context and semantics which draws on the whole of human knowledge and experience, and would thus require advanced pattern recognition and artificial intelligence technologies to be implemented on a computer. This problem overlaps to some extent with the problem of Text Segmentation that occurs in some languages which are traditionally written without inter-word spaces, like Chinese and Japanese . However, even for those languages, text segmentation is often much easier than speech segmentation, because the written language usually has little interference between adjacent words, and often contains additional clues not present in speech (such as the use of Chinese Characters for word stems in Japanese). REFERENCES
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