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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.
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.
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 .
G.729 is a royalty-free [1] narrow-band vocoder-based audio data compression algorithm using a frame length of 10 milliseconds. It is officially described as Coding of speech at 8 kbit/s using code-excited linear prediction speech coding (CS-ACELP), and was introduced in 1996. [2]
A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.
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 .