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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.
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).
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.
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.
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.
Early 1970s vocoder, custom-built for electronic music band Kraftwerk. A vocoder (/ ˈ v oʊ k oʊ d ər /, a portmanteau of voice and encoder) is a category of speech coding that analyzes and synthesizes the human voice signal for audio data compression, multiplexing, voice encryption or voice transformation.
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.
AMR-WB is codified as G.722.2, an ITU-T standard speech codec, formally known as Wideband coding of speech at around 16 kbit/s using Adaptive Multi-Rate Wideband (AMR-WB). G.722.2 AMR-WB is the same codec as the 3GPP AMR-WB. The corresponding 3GPP specifications are TS 26.190 for the speech codec [3] and TS 26.194 for the Voice Activity ...