In celebration of our three month anniversary with PBX in a Flash, Nerd Vittles today introduces new PBX in a Flash hosted service and five new applications using Cepstral’s Allison for text-to-speech with Asterisk 1.4. Combining both classifiers, the best accuracy obtained is 98.23% +/- 0.001. Text-to-Speech Bonanza with Cepstral and Asterisk 1.4.
In this paper, we proposed a fundamental frequency prediction method which is used primarily in the voice conversion system.
The classification was carried out in two steps using, first, a generative and, later, a discriminative approach. A Gaussian Mixture Model (GMM) is established to predict the fundamental frequency based on the Linear Predictive Cepstral Coefficient (LPCC) to predict pitch in whispered speech conversion system and voice conversion system. ReadSpeaker provides lifelike online and offline text-to-speech solutions to make your products and services more engaging. 49 patients presenting a vocal cord motility impairment between 20 were included. Study design This is a retrospective cohort study. Moreover, this paper uses a strategy based on combining classifiers for fusing the nonlinear analysis with the information provided by classic parameterization approaches found in the literature (noise parameters and mel-frequency cepstral coefficients). Objectives To determine the usefulness of the smoothed cepstral peak prominence (CPPS) in sustained vowel as objective measure of dysphonia. We make realistic synthetic voices that say anything, anywhere, with personality and style. Two of these features are based on conventional nonlinear statistics (largest Lyapunov exponent and correlation dimension), two are based on recurrence and fractal-scaling analysis, and the remaining are based on different estimations of the entropy. At Cepstral, Text-to-Speech is our only focus.
The paper addresses the discrimination capabilities of 11 features extracted using nonlinear analysis of time series. This paper proposes a new approach to improve the amount of information extracted from the speech aiming to increase the accuracy of a system developed for the automatic detection of pathological voices.