Non-Intrusive speech quality assessment (NISQA) has gained significant attention for predicting the mean opinion score (MOS) of speech without requiring the reference speech. In practical NISQA scenarios, untrusted third-party resources are often employed during deep neural network training to reduce costs. However, it would introduce a potential security vulnerability as specially designed untrusted resources can launch backdoor attacks against NISQA systems. Existing backdoor attacks primarily focus on classification tasks and are not directly applicable to NISQA which is a regression task. In this paper, we propose a novel backdoor attack on NISQA tasks, leveraging presence events as triggers to achieving highly stealthy attacks. To evaluate the effectiveness of our proposed approach, we conducted experiments on four benchmark datasets and employed two state-of-the-art NISQA models. The results demonstrate that the proposed backdoor attack achieved an average attack success rate of up to 99% with a poisoning rate of only 3%.

Current state-of-the-art methods for automatic synthetic speech evaluation are based on MOS prediction neural models. Such MOS prediction models include MOSNet and LDNet that use spectral features as input, and SSL-MOS that relies on a pretrained self-supervised learning model that directly uses the speech signal as input. In modern high-quality neural TTS systems, prosodic appropriateness with regard to the spoken content is a decisive factor for speech naturalness. For this reason, we propose to include prosodic and linguistic features as additional inputs in MOS prediction systems, and evaluate their impact on the prediction outcome. We consider phoneme level F0 and duration features as prosodic inputs, as well as Tacotron encoder outputs, POS tags and BERT embeddings as higher-level linguistic inputs. All MOS prediction systems are trained on SOMOS, a neural TTS-only dataset with crowdsourced naturalness MOS evaluations. Results show that the proposed additional features are beneficial in the MOS prediction task, by improving the predicted MOS scores' correlation with the ground truths, both at utterance-level and system-level predictions.