Affective computingAffective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer science, psychology, and cognitive science. While some core ideas in the field may be traced as far back as to early philosophical inquiries into emotion, the more modern branch of computer science originated with Rosalind Picard's 1995 paper on affective computing and her book Affective Computing published by MIT Press.
Discriminative modelDiscriminative models, also referred to as conditional models, are a class of logistical models used for classification or regression. They distinguish decision boundaries through observed data, such as pass/fail, win/lose, alive/dead or healthy/sick. Typical discriminative models include logistic regression (LR), conditional random fields (CRFs) (specified over an undirected graph), decision trees, and many others. Typical generative model approaches include naive Bayes classifiers, Gaussian mixture models, variational autoencoders, generative adversarial networks and others.
OrthographyAn orthography is a set of conventions for writing a language, including norms of spelling, hyphenation, capitalization, word boundaries, emphasis, and punctuation. Most transnational languages in the modern period have a writing system, and most of these systems have undergone substantial standardization, thus exhibiting less dialect variation than the spoken language. These processes can fossilize pronunciation patterns that are no longer routinely observed in speech (e.g.
Particle filterParticle filters, or sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to find approximate solutions for filtering problems for nonlinear state-space systems, such as signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made and random perturbations are present in the sensors as well as in the dynamical system.
Lip readingLip reading, also known as speechreading, is a technique of understanding speech by visually interpreting the movements of the lips, face and tongue when normal sound is not available. It relies also on information provided by the context, knowledge of the language, and any residual hearing. Although lip reading is used most extensively by deaf and hard-of-hearing people, most people with normal hearing process some speech information from sight of the moving mouth.
AphasiaIn aphasia, a person may be unable to comprehend or unable to formulate language because of damage to specific brain regions. The major causes are stroke and head trauma; prevalence is hard to determine but aphasia due to stroke is estimated to be 0.1–0.4% in the Global North. Aphasia can also be the result of brain tumors, epilepsy, brain damage and brain infections, or neurodegenerative diseases (such as dementias). To be diagnosed with aphasia, a person's language must be significantly impaired in one (or more) of the four aspects of communication.
Writing systemA writing system is a method of visually representing verbal communication, based on a script and a set of rules regulating its use. While both writing and speech are useful in conveying messages, writing differs in also being a reliable form of information storage and transfer. Writing systems require shared understanding between writers and readers of the meaning behind the sets of characters that make up a script.
Data PreprocessingData preprocessing can refer to manipulation or dropping of data before it is used in order to ensure or enhance performance, and is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues. Analyzing data that has not been carefully screened for such problems can produce misleading results.
Multiple sequence alignmentMultiple sequence alignment (MSA) may refer to the process or the result of sequence alignment of three or more biological sequences, generally protein, DNA, or RNA. In many cases, the input set of query sequences are assumed to have an evolutionary relationship by which they share a linkage and are descended from a common ancestor. From the resulting MSA, sequence homology can be inferred and phylogenetic analysis can be conducted to assess the sequences' shared evolutionary origins.
Broca's areaBroca's area, or the Broca area (ˈbroʊkə, also UKˈbrɒkə, USˈbroʊkɑː), is a region in the frontal lobe of the dominant hemisphere, usually the left, of the brain with functions linked to speech production. Language processing has been linked to Broca's area since Pierre Paul Broca reported impairments in two patients. They had lost the ability to speak after injury to the posterior inferior frontal gyrus (pars triangularis) (BA45) of the brain.
Expectation–maximization algorithmIn statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step.
