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In the process of encoding, the sender (i.e. encoder) uses verbal (e.g. words, signs, images, video) and non-verbal (e.g. body language, hand gestures, face expressions) symbols for which he or she believes the receiver (that is, the decoder) will understand. The symbols can be words and numbers, images, face expressions, signals and/or actions.
In June 2018, Google proposed to use pre-trained speaker verification models as speaker encoders to extract speaker embeddings. [14] The speaker encoders then become part of the neural text-to-speech models, so that it can determine the style and characteristics of the output speech.
The T5 encoder can be used as a text encoder, much like BERT. It encodes a text into a sequence of real-number vectors, which can be used for downstream applications. For example, Google Imagen [26] uses T5-XXL as text encoder, and the encoded text vectors are used as conditioning on a diffusion model.
The channel is the means used to send the message. The receiver is the audience for whom the message is intended. They have to decode it to understand it. [4] [30] Despite the emphasis on only four basic components, Berlo initially identifies a total of six components. The two additional components are encoder and decoder. [31]
Linear predictive coding (LPC) is a method used mostly in audio signal processing and speech processing for representing the spectral envelope of a digital signal of speech in compressed form, using the information of a linear predictive model. [1] [2] LPC is the most widely used method in speech coding and speech synthesis.
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [ 1 ] [ 2 ] It learns to represent text as a sequence of vectors using self-supervised learning .
Langacker’s Cognitive Grammar. The term ‘usage-based’ was coined by Ronald Langacker in 1987, while doing research on Cognitive Grammar. Langacker identified commonly recurring linguistic patterns (patterns such as those associated with Wh- fronting, subject-verb agreement, the use of present participles, etc.) and represented these ...
The problem of finding a smallest grammar for an input sequence (smallest grammar problem) is known to be NP-hard, [2] so many grammar-transform algorithms are proposed from theoretical and practical viewpoints. Generally, the produced grammar is further compressed by statistical encoders like arithmetic coding.