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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).
Lexical recognition is of particular value in the field of computer speech recognition, since the ability to build and search a network of semantically connected ideas would greatly increase the effectiveness of speech-recognition software. Statistical models can be used to segment and align recorded speech to words or phones.
For example, if learners do not know the word grandfather they may paraphrase it by saying "my father's father". Semantic avoidance Learners may avoid a problematic word by using a different one, for example substituting the irregular verb make with the regular verb ask. The regularity of "ask" makes it easier to use correctly. [2] Word coinage
Speech processing is the study of speech signals and the processing methods of signals. The signals are usually processed in a digital representation, so speech processing can be regarded as a special case of digital signal processing, applied to speech signals. Aspects of speech processing includes the acquisition, manipulation, storage ...
In speaker diarisation, one of the most popular methods is to use a Gaussian mixture model to model each of the speakers, and assign the corresponding frames for each speaker with the help of a Hidden Markov Model. There are two main kinds of clustering strategies. The first one is by far the most popular and is called Bottom-Up.
One theory is that learning is incremental and that the recall of each word pair is strengthened with repetition. Another theory suggests that learning is all-or-none, that is one learns the word pair in a single trial and memory performance is due to the average learned pairs, some of which are learned on earlier trials and some on later trials.
For example, masked repetition priming, modality match during study and test, and the use of easy word-fragments in word-fragment recall are all perceptual manipulations which increase know responses. [15] An example of a conceptual manipulation which enhances know responses is when a prime item is semantically related to a target item. [15]
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]