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  2. Encoding/decoding model of communication - Wikipedia

    en.wikipedia.org/wiki/Encoding/decoding_model_of...

    Use (distribution or consumption) – For a message to be successfully "realized", "the broadcasting structures must yield encoded messages in the form of a meaningful discourse." [ 3 ] This means that the message has to be adopted as a meaningful discourse and it has to be meaningfully decoded.

  3. Code-excited linear prediction - Wikipedia

    en.wikipedia.org/wiki/Code-excited_linear_prediction

    Code-excited linear prediction (CELP) is a linear predictive speech coding algorithm originally proposed by Manfred R. Schroeder and Bishnu S. Atal in 1985. At the time, it provided significantly better quality than existing low bit-rate algorithms, such as residual-excited linear prediction (RELP) and linear predictive coding (LPC) vocoders (e.g., FS-1015).

  4. Source–message–channel–receiver model of communication

    en.wikipedia.org/wiki/Source–message–channel...

    It is the medium and process of how the message is transmitted. Berlo discusses it primarily in terms of the five senses used to decode messages: seeing, hearing, touching, smelling, and tasting. Depending on the message, some channels are more useful than others. It is often advantageous to use several channels simultaneously.

  5. Linear predictive coding - Wikipedia

    en.wikipedia.org/wiki/Linear_predictive_coding

    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.

  6. BERT (language model) - Wikipedia

    en.wikipedia.org/wiki/BERT_(language_model)

    High-level schematic diagram of BERT. It takes in a text, tokenizes it into a sequence of tokens, add in optional special tokens, and apply a Transformer encoder. The hidden states of the last layer can then be used as contextual word embeddings. BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules:

  7. Deep learning speech synthesis - Wikipedia

    en.wikipedia.org/wiki/Deep_learning_speech_synthesis

    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.

  8. Speech coding - Wikipedia

    en.wikipedia.org/wiki/Speech_coding

    Speech coding differs from other forms of audio coding in that speech is a simpler signal than other audio signals, and statistical information is available about the properties of speech. As a result, some auditory information that is relevant in general audio coding can be unnecessary in the speech coding context.

  9. Signing Exact English - Wikipedia

    en.wikipedia.org/wiki/Signing_Exact_English

    The use of Signing Exact English has been controversial but in 2012 was suggested by Dr. Marc Marschark (editor of Deaf Studies and Deaf Education) as a viable support to listening, speech, English language, and reading in the schools. [citation needed] Some deaf people [who?] find SEE to be difficult to efficiently perceive and produce. Deaf ...