The best method in terms of spectral fit and computational complexity is then applied to a CELP-type speech coding algorithm, with results which are superior to conventional AR models.YimS.SenD.Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on...
In this algorithm, a spectral noise bias is calculated from segments of speech inactivity and is subtracted from noisy speech spectral amplitude, retaining the phase as it is. Secondary procedures follow spectral subtraction to reduce the unpleasant auditory effects due to spectral error. The draw...
This algorithm was conditioned on audio from a short video clip of her speaking in a pre-injury video. The algorithm transformed the decoded waveforms to be in her personalized voice, which we then used to drive the avatar animation. We synchronized the avatar animation and the personalized ...
In the design of low-bit-rate (LBR) speech coding algorithms, language variability is often considered to be of secondary importance in comparison with other operational factors such as speaker variability and noise. Given that languages differ extensively in the composition of the spectral envelope ...
Recall that the Viterbi algorithm finds the state sequence that has the highest probability of being taken while generating the observation sequence. The final score is that probability normalized by the number of input observations, T. The Figure 8 below shows the result: The distributions look ...
Comparison of evaluation results between the baseline system and the algorithm in this paper. This paper believes that Praat has produced valuable data. Besides, we also calculate the accuracy of Chinese phonetic production, and the results are shown in the figure below. As shown in the histogram...
The KNN classifier model obtained a peak accuracy of 94.5% in comparison to other traditional models. Jun and Kim (2018); Bahn, (2020) propose a new algorithm for fully automatic computer-aided detection of Pathological voice dysfluencies with state-of-the-art machine learning models and a ...
To invert the generative model, we used the Dynamic Expectation Maximisation algorithm55,64, which is based on top-down predictions and bottom-up prediction errors. We considered a generative model with two hierarchically related levels. At each level i in the hierarchy, dynamics are determined by...
The present invention remarkably improves speech quality in comparison with synthesized speech of the prior art by improving the coding method for storing the speech segments in the speech segment concatenation subsystem (3). A description with respect to the operation of the speech segment concatenatio...
FIG. 2 is a block diagram of another example of a speech coding apparatus according to the invention. In this example, the acoustic feature value measure 10, the prototype vector signal store 12, the comparison processor 14, the model match score processor 18, and the speech transition match...