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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.
Although the envelope/fine-structure dichotomy has been debated and questions remain as to how temporal fine structure cues are actually encoded in the auditory system, these studies have led to a range of applications in various fields including speech and audio processing, clinical audiology and rehabilitation of sensorineural hearing loss ...
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.
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.
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.
Cued speech is a hybrid, oral/manual system of communication used by some deaf or hard-of-hearing people. It is a technique that uses handshapes near the mouth ("cues") to represent phonemes that can be challenging for some deaf or hard-of-hearing people to distinguish from one another through speechreading ("lipreading") alone.
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]
In audiology, the Articulation Index (AI) is a tool used to predict the amount of speech that is audible to a patient with a specific hearing loss.The AI figure for a given patient can range from zero to one, representing the proportion of the average speech signal that is audible.