ConferencingSpeech 2022 Challenge: Non-intrusive Objective Speech Quality Assessment (NISQA) Challenge for Online Conferencing Applications

Yi, Gaoxiong; Xiao, Wei; Xiao, Yiming; Naderi, Babak; Möller, Sebastian; Wardah, Wafaa; Mittag, Gabriel; Cutler, Ross; Zhang, Zhuohuang; Williamson, Donald S.; Chen, Fei; Yang, Fuzheng; Shang, Shidong

doi:10.21437/interspeech.2022-10597

Cited by 21 publications

(5 citation statements)

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“…However, it was developed for narrow-band applications, and works on limited impairment types, but correlates poorly with human ratings [32]. More recently, DNN-based approaches have been proposed to estimate the speech quality scores [13], [14], [32]- [38]. Some of these learning-based approaches use other objective metrics as the ground truth to train their speech quality predictor.…”

Section: Related Workmentioning

confidence: 99%

ICASSP 2023 Deep Noise Suppression Challenge

Dubey,

Aazami,

Gopal

et al. 2024

IEEE Open J. Signal Process.

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The ICASSP 2023 Deep Noise Suppression (DNS) Challenge marks the fifth edition of the DNS challenge series. DNS challenges were organized from 2019 to 2023 to foster research in the field of DNS. Previous DNS challenges were held at INTERSPEECH 2020, ICASSP 2021, INTERSPEECH 2021, and ICASSP 2022 This challenge aims to advance models capable of jointly addressing denoising, dereverberation, and interfering talker suppression, with separate tracks focusing on headset and speakerphone scenarios. The challenge facilitates personalized deep noise suppression by providing accompanying enrollment clips for each test clip, each containing the primary talker only, which can be used to compute a speaker identity feature and disentangle primary and interfering speech. While the majority of models submitted to the challenge were personalized, the same teams emerged as the winners in both tracks. The best models demonstrated improvements of 0.145 and 0.141 in the challenge's score, respectively, when compared to the noisy blind test set. We present additional analysis and draw comparisons to previous challenges.

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Section: Related Workmentioning

confidence: 99%

ICASSP 2023 Deep Noise Suppression Challenge

Dubey,

Aazami,

Gopal

et al. 2024

IEEE Open J. Signal Process.

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“…Non-intrusive objective speech quality assessment tools like ITU-T P.563 [15] do not require a reference, though it has a low correlation to subjective quality [16]. Newer neural net-based methods such as [16], [17], [18], [19] provide better correlations to subjective quality. NISQA [20] is an objective metric for P.804, though the correlation to subjective quality is not sufficient to use as a challenge metric (in the ConferencingSpeech 2022 Challenge [19] NISQA was used as a baseline model and achieved a Pearson Correlation Coefficient = 0.724 to MOS).…”

Section: Related Workmentioning

confidence: 99%

ICASSP 2023 Speech Signal Improvement Challenge

Cutler,

Saabas,

Naderi

et al. 2024

IEEE Open J. Signal Process.

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The ICASSP 2023 Speech Signal Improvement Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems. The speech signal quality can be measured with SIG in ITU-T P.835 and is still a top issue in audio communication and conferencing systems. For example, in the ICASSP 2022 Deep Noise Suppression challenge, the improvement in the background and overall quality is impressive, but the improvement in the speech signal is not statistically significant. To improve the speech signal the following speech impairment areas must be addressed: coloration, discontinuity, loudness, reverberation, and noise. A training and test set was provided for the challenge, and the winners were determined using an extended crowdsourced implementation of ITU-T P.804's listening phase. The results show significant improvement was made across all measured dimensions of speech quality.

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“…Another recent challenge [24] focused on predicting speech quality in speech conferencing applications, and also saw several submissions, e.g. [25,26], making use of SSL representations.…”

Section: Quality Prediction Using Ssl Modelsmentioning

confidence: 99%

On the Use of Self-Supervised Speech Representations in Spontaneous Speech Synthesis

Wang,

Henter,

Gustafson

et al. 2023

12th ISCA Speech Synthesis Workshop (SSW2023)

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Self-supervised learning (SSL) speech representations learned from large amounts of diverse, mixed-quality speech data without transcriptions are gaining ground in many speechtechnology applications. Prior work has shown that SSL is an effective intermediate representation in two-stage text-tospeech (TTS) for both read and spontaneous speech. However, it is still not clear which SSL and which layer from each SSL model is most suited for spontaneous TTS. We address this shortcoming by extending the scope of comparison for SSL in spontaneous TTS to 6 different SSLs and 3 layers within each SSL. Furthermore, SSL has also shown potential in predicting the mean opinion scores (MOS) of synthesized speech, but this has only been done in read-speech MOS prediction. We extend an SSL-based MOS prediction framework previously developed for scoring read speech synthesis and evaluate its performance on synthesized spontaneous speech. All experiments are conducted twice on two different spontaneous corpora in order to find generalizable trends. Overall, we present comprehensive experimental results on the use of SSL in spontaneous TTS and MOS prediction to further quantify and understand how SSL can be used in spontaneous TTS. Audios samples: https: //www.speech.kth.se/tts-demos/sp_ssl_tts

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ConferencingSpeech 2022 Challenge: Non-intrusive Objective Speech Quality Assessment (NISQA) Challenge for Online Conferencing Applications

Cited by 21 publications

References 0 publications

ICASSP 2023 Deep Noise Suppression Challenge

ICASSP 2023 Deep Noise Suppression Challenge

ICASSP 2023 Speech Signal Improvement Challenge

On the Use of Self-Supervised Speech Representations in Spontaneous Speech Synthesis

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