Perceptual Objective Listening Quality Assessment (POLQA), The Third Generation ITU-T Standard for End-to-End Speech Quality Measurement Part I-Temporal Alignment

By J. Beerends, Christian Schmidmer, J. Berger, Matthias Obermann, Raphael Ullmann, Joachim Pomy, M. Keyhl. 2013

205 citations recorded by the source corpus.

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Citations in the corpus

Dnsmos: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors (2020, background): “Conventional objec-tive speech quality metrics such as Perceptual Evaluation of Speech Quality (PESQ) [2], Perceptual Objective Listening Quality Analysis (POLQA) [3] and Signal to Distortion Ratio (SDR) are widely used to evaluate Speech Enhancement (SE) algorithms optimized for human perception.”
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era (2022, unknown): “In the case of TTS, for example, BSD [171], PESQ [172], POLQA [173], or ITU-T Rec.”
Semantic Communications: Principles and Challenges (2021, methodology): “Therefore, the metrics, such as perceptual evaluation of speech quality (PESQ) [45], short-time objective intelligibility (STOI) [46], and perceptual objective listening quality assessment (POLQA) [47], can be adopted to measure the global semantic content of speech signals, which comprehensively ev”
Intrusive and Non-Intrusive Perceptual Speech Quality Assessment Using a Convolutional Neural Network (2019, background): “862 was proposed due to the advent of wideband telephone services [6, 7].”
A Streamwise Gan Vocoder for Wideband Speech Coding at Very Low Bit Rate (2021, unknown): “Since it is known that objective speech quality models like POLQA [27] are not reliable for non-waveform-preserving coding schemes, and in particular for neural decoders, we also considered the newly introduced objective metric WARP-Q [28], which was designed for this purpose.”
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework (2020, background): “Most of the published literature report experimental results based on objective speech quality metrics such as Perceptual Evaluation of Speech Quality (PESQ), Perceptual Objective Listening Quality Analysis (POLQA) [11], Virtual Speech Quality Objective Listener (ViSQOL) [12], Speech to Distortion R”
A scalable noisy speech dataset and online subjective test framework (2019, methodology): “To demonstrate the dataset and evaluation framework we apply it to several noise suppressors and compare the subjective Mean Opinion Score (MOS) with objective quality measures such as SNR, PESQ, POLQA, and VISQOL and show why MOS is still required.”