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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 Harvard sentences, or Harvard lines, [1] is a collection of 720 sample phrases, divided into lists of 10, used for standardized testing of Voice over IP, cellular, and other telephone systems. They are phonetically balanced sentences that use specific phonemes at the same frequency they appear in English.
Semantic Interpretation for Speech Recognition (SISR) defines the syntax and semantics of annotations to grammar rules in the Speech Recognition Grammar Specification (SRGS). Since 5 April 2007, it is a World Wide Web Consortium recommendation.
A spoken dialog system (SDS) is a computer system able to converse with a human with voice.It has two essential components that do not exist in a written text dialog system: a speech recognizer and a text-to-speech module (written text dialog systems usually use other input systems provided by an OS).
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).
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]