LanguageLanguage is a structured system of communication that consists of grammar and vocabulary. It is the primary means by which humans convey meaning, both in spoken and written forms, and may also be conveyed through sign languages. The vast majority of human languages have developed writing systems that allow for the recording and preservation of the sounds or signs of language. Human language is characterized by its cultural and historical diversity, with significant variations observed between cultures and across time.
Speech synthesisSpeech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech. The reverse process is speech recognition. Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database.
Language deathIn linguistics, language death occurs when a language loses its last native speaker. By extension, language extinction is when the language is no longer known, including by second-language speakers, when it becomes known as an extinct language. A related term is linguicide, the death of a language from natural or political causes, and, rarely, glottophagy, the absorption or replacement of a minor language by a major language.
Second languageA second language (L2) is a language spoken in addition to one's first language (L1). A second language may be a neighbouring language, another language of the speaker's home country, or a foreign language. A speaker's dominant language, which is the language a speaker uses most or is most comfortable with, is not necessarily the speaker's first language. For example, the Canadian census defines first language for its purposes as "the first language learned in childhood and still spoken", recognizing that for some, the earliest language may be lost, a process known as language attrition.
Language acquisitionLanguage acquisition is the process by which humans acquire the capacity to perceive and comprehend language (in other words, gain the ability to be aware of language and to understand it), as well as to produce and use words and sentences to communicate. Language acquisition involves structures, rules, and representation. The capacity to use language successfully requires one to acquire a range of tools including phonology, morphology, syntax, semantics, and an extensive vocabulary.
Language revitalizationLanguage revitalization, also referred to as language revival or reversing language shift, is an attempt to halt or reverse the decline of a language or to revive an extinct one. Those involved can include linguists, cultural or community groups, or governments. Some argue for a distinction between language revival (the resurrection of an extinct language with no existing native speakers) and language revitalization (the rescue of a "dying" language).
Speech perceptionSpeech perception is the process by which the sounds of language are heard, interpreted, and understood. The study of speech perception is closely linked to the fields of phonology and phonetics in linguistics and cognitive psychology and perception in psychology. Research in speech perception seeks to understand how human listeners recognize speech sounds and use this information to understand spoken language.
Extinct languageAn extinct language is a language that no longer has any speakers, especially if the language has no living descendants. In contrast, a dead language is one that is no longer the native language of any community, even if it is still in use, like Latin. A dormant language is a dead language that still serves as a symbol of ethnic identity to a particular group. These languages are often undergoing a process of revitalisation. Languages that currently have living native speakers are sometimes called modern languages to contrast them with dead languages, especially in educational contexts.
Constructed languageA constructed language (shortened to a conlang) is a language whose phonology, grammar, and vocabulary, instead of having developed naturally, are consciously devised for some purpose, which may include being devised for a work of fiction. A constructed language may also be referred to as an artificial, planned or invented language, or (in some cases) a fictional language. Planned languages (or engineered languages/engelangs) are languages that have been purposefully designed; they are the result of deliberate, controlling intervention and are thus of a form of language planning.
Types of artificial neural networksThere are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.
Language familyA language family is a group of languages related through descent from a common ancestral language or parental language, called the proto-language of that family. The term "family" reflects the tree model of language origination in historical linguistics, which makes use of a metaphor comparing languages to people in a biological family tree, or in a subsequent modification, to species in a phylogenetic tree of evolutionary taxonomy. Linguists therefore describe the daughter languages within a language family as being genetically related.
Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Recurrent neural networkA recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.
Indigenous languageAn indigenous language, or autochthonous language, is a language that is native to a region and spoken by indigenous peoples. This language is from a linguistically distinct community that originated in the area. Indigenous languages are not necessarily national languages but they can be; for example, Aymara is an official language of Bolivia. Also, national languages are not necessarily indigenous to the country.
Artificial neural networkArtificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.
Spoken languageA spoken language is a language produced by articulate sounds or (depending on one's definition) manual gestures, as opposed to a written language. An oral language or vocal language is a language produced with the vocal tract in contrast with a sign language, which is produced with the body and hands. The term "spoken language" is sometimes used to mean only oral languages, especially by linguists, excluding sign languages and making the terms 'spoken', 'oral', 'vocal language' synonymous.
UtteranceIn spoken language analysis, an utterance is a continuous piece of speech, often beginning and ending with a clear pause. In the case of oral languages, it is generally, but not always, bounded by silence. Utterances do not exist in written language; only their representations do. They can be represented and delineated in written language in many ways. In oral/spoken language, utterances have several characteristics such as paralinguistic features, which are aspects of speech such as facial expression, gesture, and posture.
Extreme learning machineExtreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need to be tuned. These hidden nodes can be randomly assigned and never updated (i.e. they are random projection but with nonlinear transforms), or can be inherited from their ancestors without being changed.
Convolutional neural networkConvolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
