Sentence examples for speech observations from inspiring English sources

In the following, we develop a method to map the distribution of reverberant speech observations to the clean speech prior.

The newly proposed methods, such as template pruning and filtering [19], template-like dimension reduction of speech observations [20], and template matching in the second-pass decoding search [21], are beginning to address this problem.

The number "1" denotes that the speech observation is detected; "0" denotes that the noise observation is detected.

Subspace filtering methods are based on the orthogonal decomposition of the noisy speech observation space into a signal subspace and a noise subspace.

In DT-based acoustic modeling, DTs are used to determine the state likelihood by asking a series of questions about the current speech observation.

(11 where ( {widehat{lambda}}_dleft t,kright) ) represents the noise level (noise power spectra) and ( {Y}_k^2left t,kright) ) represents the power spectra of noisy speech (observation value).

In the DNN-based speech coefficient mapping, parallel training data of reverberant speech (observation) and clean speech (desired output) are used to train the DNN to predict clean speech.

The result is in line with the results of Murakami et al. (2011) who found enhanced lip MEPs during speech observation relative to observation of lateral jaw movements.

In the first iteration step, we use the original reverberant speech as observations, and in the second step, we use the dereverberated estimate from the first iteration round.

For example, during rTMS-induced disruption of the lip representation in the M1 cortex participants are able to move their lips and unsurprisingly comprehend clear speech (unpublished observations).

For example, some might label as hate speech the observation of Mr. Villasenor that "[t]he very significant gender variation in the responses [to the question 'does the First Amendment protect "hate speech"?'] is also noteworthy". The strength of this country lies in vigorous debate about policy issues.