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The number of target speakers is 260.
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A few methods without the requirement of target speaker's emotional speech have been studied.
Subsequently, the source speaker characteristics are transformed to that of target speaker using mapping function developed in the training phase[3].
In [19], the NMF was applied for single-channel speech separation where the speech of target speaker over that of masking speaker was enhanced by using sparse dictionaries learned on a phoneme level for individual speakers.
Compared to an exhaustive search, the computational complexity is greatly reduced from O T K4) to O T K2). Figure 2 SNR gains of target speaker at different input SNR conditions with beam width varying from 1 to 256.
The entity in the intersection of row i and column j, denoted by (s ij e ij ), means that a listener in group i listens to utterance set j synthesized via the model set of target speaker s ij for the emotion e ij.
The MBMDs between the neutral models of the pool speakers and neutral models of the target speakers are summarized in Table 3.
Dynamic programming approach tries to model the long-term behaviour of LSFs of the target speaker, while it is trying to preserve the relationship between the subsequent frames of the source LSFs, during transformation.
To compute the MVDR beamformer filter coefficients, an estimate (hat {theta }) of the DOA of the target speaker as well as an estimate of the noise coherence matrix is required.
In [19], for example, the authors model the spectral relationships between two arbitrary speakers (reference speakers) using GMMs and convert the source speaker's speech using the matrix that projects the feature space of the source speaker into that of the target speaker through that of the reference speakers.
The result is considered as sound for the number of impostor and target speakers used in the test.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com