Sentence examples for broad phoneme classes from inspiring English sources

To characterize the speech message, phoneme labels from the TIMIT corpus are first divided into four broad phoneme classes.

To explain the results, a measure of similarity between phoneme classes from DNN activations is proposed and linked to their acoustic properties.

As a result, five output files for five phoneme classes were produced.

The best-fitted candidates for different phoneme classes with LPC feature was mostly MGD or MGLD with p = 0.25.

The evaluation results of the second experimental setup benefit the statistical-based speech recognition and synthesis algorithms statistically modeling phoneme classes.

Also, the results confirm that the distribution of the different phoneme classes is better statistically modeled by a mixture of Gaussian and Laplace pdfs.

This technique shows significant improvement in voiced phoneme class without affecting the performance of unvoiced phoneme class.

For the second setup, the number of frames, N, corresponding to the segment lengths for each phoneme class is shown in Table 3.

For each phoneme class, the relevant information was first extracted from the 100 sentences used in the first experimental setup and then concatenated to produce one file.

In each table, there are 15 blocks surrounded by bold lines belonging to each phoneme class with a determined frame length.

Broad Phonetic Classes for Speaker Verification with Noisy, Large-Scale Data.