Sentence examples for model for speech from inspiring English sources

Figure 3 A hidden Markov model for speech recognition.

Starting from a classical HMM-based model for speech, we study how the number of Gaussians impacts the system performance.

According to such theory, Maragos et al. proposed an AM-FM modulation model for speech analysis, synthesis and coding.

Since speech signals exhibit both of these properties, we hypothesize that CNNs are a more effective model for speech compared to Deep Neural Networks (DNNs).

This decomposition is possible under the assumption of a low-rank model for speech, and on the availability of an estimate of the noise correlation matrix.

The CASA system proposed by Brown and Cooke employs maps of different auditory features that generated from the output of a cochlear model for speech segregation.

We investigate the use of ensemble methods in the class ofstatic mixture models for speech recognition.

In this paper, we have introduced the notion of context-dependent (CD) models for speech enhancement methods that use trained models of speech and noise parameters.

Then, the weights of the speech atoms are used as phonetic scores (instead of the likelihoods of hidden Markov models) for speech recognition.

In this work, we proposed a novel approach for the extraction of turn-level features using latent topic models for speech emotion recognition.

The parameters of the source location feature models for speech and noise are estimated on a per-utterance basis from the multi-channel output of the WPE.