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Speech recognition is an interdisciplinary subfield of ... In the long history of speech recognition, both shallow form and deep form (e.g. recurrent nets) of ...
These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space , typically of several hundred dimensions , with each unique word in the corpus being assigned a corresponding vector in the space.
Item recognition can be modeled using Multiple trace theory and the attribute-similarity model. [63] In brief, every item that one sees can be represented as a vector of the item's attributes, which is extended by a vector representing the context at the time of encoding, and is stored in a memory matrix of all items ever seen.
Speech corpus – database of speech audio files and text transcriptions. In Speech technology, speech corpora are used, among other things, to create acoustic models (which can then be used with a speech recognition engine). In Linguistics, spoken corpora are used to do research into phonetic, conversation analysis, dialectology and other fields.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
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).
Kaldi is an open-source speech recognition toolkit written in C++ for speech recognition and signal processing, freely available under the Apache License v2.0.. Kaldi aims to provide software that is flexible and extensible, [2] and is intended for use by automatic speech recognition (ASR) researchers for building a recognition system.
A language model is a probabilistic model of a natural language. [1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.