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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).
This theory can also be extended for the speech recognition domain. As an example, in [15] an extension of a theory for unsupervised learning of invariant visual representations to the auditory domain and empirically evaluated its validity for voiced speech sound classification was proposed. Authors empirically demonstrated that a single-layer ...
A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.
British scientist John Morton's logogen model was designed to explain word recognition using a new type of unit known as a logogen. A critical element of this theory is the involvement of lexicons, or specialized aspects of memory that include semantic and phonemic information about each item that is contained in memory. A given lexicon ...
The lexical route is the process whereby skilled readers can recognize known words by sight alone, through a "dictionary" lookup procedure. [1] [4] According to this model, every word a reader has learned is represented in a mental database of words and their pronunciations that resembles a dictionary, or internal lexicon.
However, despite these differences, the core problem of speech recognition is the same for both humans and machines: namely, of finding the best match between a given speech sound and its corresponding word string. Automatic speech recognition technology attempts to simulate and optimize this process computationally." [75]
The cohort model is based on the concept that auditory or visual input to the brain stimulates neurons as it enters the brain, rather than at the end of a word. [5] This fact was demonstrated in the 1980s through experiments with speech shadowing, in which subjects listened to recordings and were instructed to repeat aloud exactly what they heard, as quickly as possible; Marslen-Wilson found ...
Explanation-based learning (EBL) is a form of machine learning that exploits a very strong, or even perfect, domain theory (i.e. a formal theory of an application domain akin to a domain model in ontology engineering, not to be confused with Scott's domain theory) in order to make generalizations or form concepts from training examples. [1]