Search results
Results from the WOW.Com Content Network
Deep learning speech synthesis refers to the application of deep learning models to generate natural-sounding human speech from written text (text-to-speech) or spectrum . Deep neural networks are trained using large amounts of recorded speech and, in the case of a text-to-speech system, the associated labels and/or input text.
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).
It is a system of coded meanings, and in order to create that, the sender needs to understand how the word is comprehensible to the members of the audience. 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 ...
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 .
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.
Writing systems are used to record human language, and may be classified according to certain common features. The usual name of the script is given first; the name of the languages in which the script is written follows (in brackets), particularly in the case where the language name differs from the script name.
Whisper is a machine learning model for speech recognition and transcription, created by OpenAI and first released as open-source software in September 2022. [2]It is capable of transcribing speech in English and several other languages, and is also capable of translating several non-English languages into English. [1]