This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
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). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.
Natural-language user interface (LUI or NLUI) is a type of computer human interface where linguistic phenomena such as verbs, phrases and clauses act as UI controls for creating, selecting and modifying data in software applications. In interface design, natural-language interfaces are sought after for their speed and ease of use, but most suffer the challenges to understanding wide varieties of ambiguous input. Natural-language interfaces are an active area of study in the field of natural-language processing and computational linguistics.
In linguistics, mutual intelligibility is a relationship between languages or dialects in which speakers of different but related varieties can readily understand each other without prior familiarity or special effort. It is sometimes used as an important criterion for distinguishing languages from dialects, although sociolinguistic factors are often also used. Intelligibility between languages can be asymmetric, with speakers of one understanding more of the other than speakers of the other understanding the first.
In recent works, the use of phone class-conditional posterior probabilities (posterior features) directly as features provided successful results in template-based ASR systems. In this paper, motivated by the high quality of current text-to-speech systems ...
In recent works, the use of phone class-conditional posterior probabilities (posterior features) directly as features provided successful results in template-based ASR systems. Moreover, it has been shown that these features tend to be sparse and orthogona ...
Recently, the use of phoneme class-conditional probabilities as features (posterior features) for template-based ASR has been proposed. These features have been found to generalize well to unseen data and yield better systems than standard spectral-based f ...