Sentence examples for clean utterance from inspiring English sources

This pool of data consisted of 25 pairs of clean utterance and simulated (noisy) reverberant utterance.

In training, each clean utterance is normalized to a 60-dB energy level, and the log spectra are calculated as described in Section 2.1.

Specifically, for each speaker, we can estimate the most likely Gaussian index for each frame in a clean utterance using a MAP estimator.

This is possible in Aurora 2.0 because clean and noisy speech data are "parallel", i.e. each noisy utterance has a corresponding clean utterance.

That is, the VAD results for each clean utterance are directly applied to its various noise-corrupted counterparts to implement the magnitude spectrum enhancement.

Assuming that the VAD error of MSE for a clean utterance is small and negligible, the recognition accuracy difference between MSE o)and MSE for noise-corrupted utterances can be viewed as a consequence of the VAD error due to noise.

The GMMs for the speaker identification system was trained using 500 clean utterances (100 speakers, 5 utterances for each speaker).

Another 645 clean utterances covering 20 males and 17 females were used to construct the test set with real RIRs.

The second set of clean utterances were from a speaker different from the one involved in training the CD codebook.

Reverberant speech is generated from the clean WSJCAM0 training data by convolving the clean utterances with measured room impulse responses and adding recorded background noise.

In the 'clean-condition training, multi-condition testing' evaluation task defined in [12], the training data consist of 8,440 noise-free clean utterances filtered with a G.712 filter.