Sentence examples for segregated speech from inspiring English sources

(a) clean speech; (b) mixture of clean speech and cocktail party noise; (c) segregated speech by the proposed method.

To evaluate the segregated speech quality further, we use the perceptual evaluation of speech quality (PESQ) criterion further for comparison.

It shows the clean speech in Figure 10(a), mixture (mixed by cocktail party noise) in Figure 10(b) and segregated speech by proposed system in Figure 10(c).

Sinusoidal models are also employed to model speakers for joint speaker separation and identification [20], and SNR estimation can be achieved by adapting a universal background model using segregated speech [21].

O(n) is the segregated speech obtained by CASA system.

As shown in the figure, the iterative estimation improves the quality of segregated speech signals.

Figure 12 shows the PESQ scores of IBM against unprocessed mixtures (white bars), segregated speeches from proposed system (gray bars) and from the Hu and Wang model (black bars) on ten kinds of intrusions.

Comparing the results of the Hu and Wang model, the most SNR gain about 4 dB is obtained in N0 (pure tone) By analyzing the segregated speeches, we found that the Hu and Wang model groups many target units into the background.

Segregating speech from one monaural recording has proven to be very challenging.

For this purpose, we study in this paper the computational auditory scene analysis (CASA) to segregate speech from monaural mixtures.

An effective system that segregates speech from interference in monaural (single-microphone) situations can be rewarding in such problems.