A Crowdsourcing Extension of the ITU-T Recommendation P.835 with Validation

By Babak Naderi, Ross Cutler. 2020

11 citations recorded by the source corpus.

The quality of the speech communication systems, which include noise suppression algorithms, are typically evaluated in laboratory experiments according to the ITU-T Rec. P.835. In this paper, we introduce an open-source implementation of the ITU-T Rec. P.835 for the crowdsourcing approach following the ITU-T Rec. P.808 on crowdsourcing recommendations. The implementation is an extension of the P.808 Toolkit and is highly automated to avoid operational errors. To assess our evaluation method's validity, we compared the Mean Opinion Scores (MOS), calculate using ratings collected with our implementation, and the MOS values from a standard laboratory experiment conducted according to the ITU-T Rec, P.835. Results show a high validity in all three scales (average PCC = 0.961). Results of a round-robin test showed that our implementation is a highly reproducible evaluation method (PCC=1.00). Finally, we investigated the performance of five models deep noise suppression models using our P.835 implementation and show what insights can be learned.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yingying Gao
2. Tianrui Wang
3. Junlan Feng
4. Yanan Chen
5. Weibin Zhu
6. Shilei Zhang

Citations in the corpus

• Icassp 2022 Deep Noise Suppression Challenge (2022, background): “835 [6] provides three scores for each audio clip, namely speech quality (SIG), background noise quality (BAK), and overall quality (OVRL).”
• Harmonic Gated Compensation Network Plus for ICASSP 2022 DNS Challenge (2022, methodology): “835 framework [26] and Word Accuracy (WAcc) are used to evaluate the speech quality, and HGCN+ outperforms the baseline [2] with less parameters in all metrics, as shown in Table 3.”