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  2. 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.

  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. Encoding/decoding model of communication - Wikipedia

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

    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.

  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)

    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 .

  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. Unified Speech and Audio Coding - Wikipedia

    en.wikipedia.org/wiki/Unified_Speech_and_Audio...

    Unified Speech and Audio Coding (USAC) is an audio compression format and codec for both music and speech or any mix of speech and audio using very low bit rates between 12 and 64 kbit/s. [1] It was developed by Moving Picture Experts Group (MPEG) and was published as an international standard ISO / IEC 23003-3 (a.k.a. MPEG-D Part 3) [ 2 ] and ...

  9. Codec 2 - Wikipedia

    en.wikipedia.org/wiki/Codec_2

    Codec 2 is a low-bitrate speech audio codec (speech coding) that is patent free and open source. [1] Codec 2 compresses speech using sinusoidal coding, a method specialized for human speech. Bit rates of 3200 to 450 bit/s have been successfully created. Codec 2 was designed to be used for amateur radio and other high compression voice applications.