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This paper outlined the achievements of the REVERB challenge, a community-wide campaign that evaluated speech enhancement and recognition technologies in reverberant environments.
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The evaluation is conducted using the REVERB challenge corpus, designed to evaluate speech enhancement algorithms in the presence of both reverberation and noise.
The PESQ algorithm has been basically designed to evaluate speech quality over telecom networks.
To evaluate speech performance and subjective outcomes for older individuals who have hearing impairment and use digital hearing aids.
The dual task is a data-rich paradigm for evaluating speech modes of a synthetic talking head.
In general, subjective listening test is the most accurate and preferable method for evaluating speech quality and intelligibility.
Section 4 presents experiment results aimed at evaluating speech quality and watermark robustness against commonly encountered attacks.
In this respect, it was attempted to evaluate speech perception scores in children implanted before and after the age of 3 in a homogenous group.
According to [7, p. 401], this signal belongs to a database designed by the French National Agency for Telecommunications (CNET) for testing and evaluating speech recognition algorithms.
The PESQ, which is an objective measure to evaluate speech quality, correlates well with subjective test scores at mid and above mid bit rates.
This was shown to be the case by Hu and Loizou when evaluating speech enhancement algorithms with a variety of objective quality metrics [29].
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