Sentence examples for speech classification accuracy from inspiring English sources

The suggested audio-visual model significantly improves relevant speech classification accuracy compared to existing GMM-based model and the proposed AVSS algorithm improves the speech separation quality compared to reference ICA- and AVSS-based methods.

In this paper, we show that DNNs can be used to boost the classification accuracy of basic speech units, such as phonetic attributes (phonological features) and phonemes.

If we use all data for training, then it is obvious that the mismatching between the distribution of the speeches around the endpoints and the distribution of the speeches on the entire dataset will lower the classification accuracy of the data around the endpoints.

Such BN features represent a nonlinear transformation of the original input features and represent the underlying speech quite well after the DNN is trained to show a good classification accuracy.

Our initial experiments revealed that classification accuracy for different irrelevant signals is variable depending on the speaker, the speech content of signal and alignment of silent parts of coherent and incoherent signals.

The fact that there were some regions of the temporal cortex that responded more strongly to rotated than to clear speech highlights the need to account for how the relative magnitude of activation to each condition contributes to classification accuracy.

The performances are tested on two pathological speech datasets, the NKI CCRT Speech Corpus (advanced head and neck cancer) and the TORGO database (cerebral palsy or amyotrophic lateral sclerosis), by evaluating classification accuracy without overlapping subjects' data among training and test partitions.

Table 5 Classification accuracy.

Figure 6 Classification accuracy.

Average classification accuracy.

Improved classification accuracy.