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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.
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 relatively small number of the auditory hair cells is surprising when compared to other sensory cells such as the rods and cones of the visual system. Thus the loss of a lower number (in the order of thousands) of auditory hair cells can be devastating while the loss of a larger number of retinal cells (in the order to hundreds of thousands ...
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.
The encoder takes this Mel spectrogram as input and processes it. It first passes through two convolutional layers. Sinusoidal positional embeddings are added. It is then processed by a series of Transformer encoder blocks (with pre-activation residual connections). The encoder's output is layer normalized. The decoder is a standard Transformer ...
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 ...
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.
The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called "speaker-independent" [1] systems. Systems that use training are called "speaker dependent".
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