Open AccessBook
Speech Coding: with Code-Excited Linear Prediction
Reads0
Chats0
About:
The article was published on 2017-04-03 and is currently open access. It has received 47 citations till now. The article focuses on the topics: Vector sum excited linear prediction & Code-excited linear prediction.read more
Citations
More filters
Journal ArticleDOI
Convolutional Neural Networks to Enhance Coded Speech
TL;DR: Two postprocessing approaches applying convolutional neural networks either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs are proposed.
Journal ArticleDOI
A Comparison Between STRAIGHT, Glottal, and Sinusoidal Vocoding in Statistical Parametric Speech Synthesis
TL;DR: The obtained results suggest that the choice of the voice has a profound impact on the overall quality of the vocoder-generated speech, and the best vocoder for each voice can vary case by case, indicating that the waveform generation method of a vocoder is essential for quality improvements.
Journal ArticleDOI
Fast Randomization for Distributed Low-Bitrate Coding of Speech and Audio
Tom Bäckström,Johannes Fischer +1 more
TL;DR: The presented experiments demonstrate that the proposed randomizations yield uncorrelated signals, that perceptual quality is competitive, and that the complexity of the proposed methods is feasible for practical applications.
Journal ArticleDOI
Quantitative Assessment of Speech in Cerebellar Ataxia Using Magnitude and Phase Based Cepstrum.
Bipasha Kashyap,Pubudu N. Pathirana,Malcolm K. Horne,Laura Power,David J. Szmulewicz,David J. Szmulewicz +5 more
TL;DR: The use of phase-based cepstral features extracted from the modified group delay function as a complement to the features obtained from the magnitude cepstrum is explored, supporting this scheme’s suitability for monitoring CA related speech motor abnormalities.
Proceedings ArticleDOI
Dithered Quantization for Frequency-Domain Speech and Audio Coding
TL;DR: This work proposes a hybrid coding approach where low-energy samples are quantized using dithering, instead of the conventional uniform quantizer, and applies 1bit quantization in a randomized sub-space.