First, we derive a novel non-iterative speech separation approach based on the diffusion framework [50], to compute HMM and FHMM models.
In this work we propose the following contributions: First, we derive a novel non-iterative speech separation approach based on the diffusion framework [50], to compute HMM and FHMM models.
It turns out that recent approaches to bit error concealment based on a predictor are well comparable to iterative approaches in speech enhancement, such as the Kalman filter approach.
It is shown that the basic iterative algorithm used in speech coding is equivalent to the MP algorithm.
We have proposed an iterative algorithm for model-based cochannel speech separation.
For future work, the proposed algorithm can be improved by iterative estimation of pitch range and speech separation.
Speech interface design is an iterative process that requires user involvement.
As shown in the figure, the iterative estimation improves the quality of segregated speech signals.
Training part-of-speech taggers (POS-taggers) requires iterative time-consuming convergence-dependable steps, which involve either expectation maximization or weight balancing processes, depending on whether the tagger uses stochastic or neural approaches, respectively.
Also, the STE has not exploited within the iterative classification process, and by its elimination, the speech with background music which has been classified as music has been changed to speech class.
Note that the synthesis process is an iterative method thus the quality of the currently synthesized speech segment depends on the quality of the previous pitch.
