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Language recognition may refer to: Language identification; Natural-language understanding; Speech recognition This page was last edited on 1 ...
The most likely language is the one with the model that is most similar to the model from the text needing to be identified. This approach can be problematic when the input text is in a language for which there is no model. In that case, the method may return another, "most similar" language as its result.
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
This language recognition chart presents a variety of clues one can use to help determine the language in which a text is written. Characters The language of a ...
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).
With James H. Martin, he wrote the textbook Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics; Roger Schank – introduced the conceptual dependency theory for natural-language understanding. [23] Jean E. Fox Tree – Alan Turing – originator of the Turing Test.
Language technology, often called human language technology (HLT), studies methods of how computer programs or electronic devices can analyze, produce, modify or respond to human texts and speech. [1] Working with language technology often requires broad knowledge not only about linguistics but also about computer science.
Speaker recognition systems fall into two categories: text-dependent and text-independent. [10] Text-dependent recognition requires the text to be the same for both enrollment and verification. [11] In a text-dependent system, prompts can either be common across all speakers (e.g. a common pass phrase) or unique.