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2.2. DNNs training with rich acoustic features Table I. The attributes of the features

Table 1 2.2. DNNs training with rich acoustic features Table I. The attributes of the features

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Figure 1: Block diagram of the proposed method In this section, we firstly introduce the framework of the proposed ABE method. Subsequently, the further details are presented. The flowchart of the proposed framework is shown in Fig. 1.
Figure 1: Block diagram of the proposed method In this section, we firstly introduce the framework of the proposed ABE method. Subsequently, the further details are presented. The flowchart of the proposed framework is shown in Fig. 1.
Fig. 2. The global variances on the training set. To address the over-smoothing problem, a globa qualization factor a@(@) is applied as follows [21]: where X,,(@) is the d-th component of a DNNs output vector at the n-th frame and M is the total number of speech frame in the training set. The global variance of the reference wideband speech features can be calculated in a similar way. Fig. 2 shows the global variances of the estimated and reference log power spectra of wideband speech in the high-band. It can be observed that the global variances of the estimated wideband speech features were smaller. Fig. 2. The global variances on the training set.
Fig. 2. The global variances on the training set. To address the over-smoothing problem, a globa qualization factor a@(@) is applied as follows [21]: where X,,(@) is the d-th component of a DNNs output vector at the n-th frame and M is the total number of speech frame in the training set. The global variance of the reference wideband speech features can be calculated in a similar way. Fig. 2 shows the global variances of the estimated and reference log power spectra of wideband speech in the high-band. It can be observed that the global variances of the estimated wideband speech features were smaller. Fig. 2. The global variances on the training set.
3.3.2. Subjective test evaluation Table III. The objective test for different ABE methods
3.3.2. Subjective test evaluation Table III. The objective test for different ABE methods
3.3. Overall evaluation
3.3. Overall evaluation
Fig. 3. The LSD for different configuration in DNNs The configurations of the DNNs were 3 hidden layers, 3072 hidden units and 11 frames expansion in propose method. Table II. The LSD with different input features
Fig. 3. The LSD for different configuration in DNNs The configurations of the DNNs were 3 hidden layers, 3072 hidden units and 11 frames expansion in propose method. Table II. The LSD with different input features
using deep architecture. Subjective evaluation indicates that proposed ABE yields a brighter sound and produce more clear than three different baseline methods. We demonstrated that proposed ABE is a promising regression model for speech, applying them to ABE. Motivated by the success of DNNs on the voice conversion and speech enhancement, we used DNNs to reconstruct log power spectra in the high-band. The resulting system clearly improves the state-of-the-art both in subjective performance evaluation and objective performance evaluation.
using deep architecture. Subjective evaluation indicates that proposed ABE yields a brighter sound and produce more clear than three different baseline methods. We demonstrated that proposed ABE is a promising regression model for speech, applying them to ABE. Motivated by the success of DNNs on the voice conversion and speech enhancement, we used DNNs to reconstruct log power spectra in the high-band. The resulting system clearly improves the state-of-the-art both in subjective performance evaluation and objective performance evaluation.
Fig. 4. Subjective test for GMM-ABE and proposed ABE
Fig. 4. Subjective test for GMM-ABE and proposed ABE
Fig. 6. Subjective test for SPE-ABE and proposed ABE
Fig. 6. Subjective test for SPE-ABE and proposed ABE
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