Sentence examples for performance of speech from inspiring English sources

The masking effect filters out unimportant signals, which may improve the performance of speech recognition systems.

This study evaluated the maximal attainable performance of speech enhancement strategies based on coherent modulation filtering.

Figurative language is crucial to the communication of states of mind other than straightforward belief, as well as to the performance of speech acts other than assertion.

The SNR and ASR results show that the new method highly improves the performance of speech enhancement algorithm based on the wavelet thresholding.

In contrast to theories that maintain that linguistic expressions have meaning in virtue of their contribution to the truth conditions of sentences where they occur, it explains linguistic meaning in terms of the use of words and sentences in the performance of speech acts.

Consequently, the performance of speech enhancement is decreased.

In summary, the results on cross transform show that long-term temporal information is important in improving the speech recognition performance of reverberant speech.

The speech recognition experiments show that emotional speech moderately affects the performance of the speech interface.

Acoustic beamforming can greatly improve the performance of Automatic Speech Recognition(ASR) and speech enhancement systems when multiple channels are available.

It is shown that the effect of stressed speech on the performance of automatic speech recognizers and automatic speaker recognizers is marginal for some types of stress (DLP), while the speaking style has a major effect.

Therefore, the performance degradation of speech recognizers in the noisy environments can be well accounted for the acoustic mismatch [3] between training and test environments.