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In linguistics, a comparative illusion (CI) or Escher sentence [a] is a comparative sentence which initially seems to be acceptable but upon closer reflection has no well-formed, sensical meaning. The typical example sentence used to typify this phenomenon is More people have been to Russia than I have .
A speech recognition grammar is a set of word patterns, and tells a speech recognition system what to expect a human to say. For instance, if you call an auto-attendant application, it will prompt you for the name of a person (with the expectation that your call will be transferred to that person's phone). It will then start up a speech ...
The comparative uses the word "mai" before the adjective, which operates like "more" or "-er" in English. For example: luminos → bright, mai luminos → brighter. To weaken the adjective, the word "puțin" (little) is added between "mai" and the adjective, for example mai puțin luminos → less bright. For absolute superlatives, the gender ...
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". [4] As this example shows, proper lexical segmentation depends on context and semantics which draws on the whole of human knowledge and experience, and would thus require advanced pattern recognition ...
Various sentences using the syllables mā, má, mǎ, mà, and ma are often used to illustrate the importance of tones to foreign learners. One example: Chinese: 妈妈骑马马慢妈妈骂马; pinyin: māma qí mǎ, mǎ màn, māma mà mǎ; lit. 'Mother is riding a horse... the horse is slow... mother scolds the horse'. [36]
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text (STT).
Two 'dialect' sentences were read by each speaker, as well as another 8 sentences selected from a larger set [3] Each sentence averages 3 seconds long and is spoken by 630 different speakers. [4] It was the first notable attempt in creating and distributing a speech corpus and the overall project has produced costs of 1.5 million US$. [5]
The success rate for part-of-speech tagging algorithms is at present much higher than that for WSD, state-of-the art being around 96% [8] accuracy or better, as compared to less than 75% [citation needed] accuracy in word sense disambiguation with supervised learning. These figures are typical for English, and may be very different from those ...