Corpus ID: 237532414

NORESQA - A Framework for Speech Quality Assessment using Non-Matching References

@article{Manocha2021NORESQAA,
  title={NORESQA - A Framework for Speech Quality Assessment using Non-Matching References},
  author={Pranay Manocha and Buye Xu and Anurag Kumar},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.08125}
}
The perceptual task of speech quality assessment (SQA) is a challenging task for machines to do. Objective SQA methods that rely on the availability of the corresponding clean reference have been the primary go-to approaches for SQA. Clearly, these methods fail in real-world scenarios where the ground truth clean references are not available. In recent years, non-intrusive methods that train neural networks to predict ratings or scores have attracted much attention, but they suffer from several… Expand
1 Citations
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TLDR
The problem of performance evaluation of reallife mixtures, where the ground truth is not available is addressed by carefully designing a blind Scale-Invariant Signal-to-Noise Ratio (SI-SNR) neural estimator, and it is shown that this estimator reliably evaluates the separation performance on real mixtures. Expand

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