Personalized Audio Quality Preference Prediction

This paper proposes to use both audio input and subject information to predict the personalized preference of two audio segments with the same content in different qualities. A siamese network is used to compare the inputs and predict the preference. Several different structures for each side of the siamese network are investigated, and an LDNet with PANNs' CNN6 as the encoder and a multi-layer perceptron block as the decoder outperforms a baseline model using only audio input the most, where the overall accuracy grows from 77.56% to 78.04%. Experimental results also show that using all the subject information, including age, gender, and the specifications of headphones or earphones, is more effective than using only a part of them.

Multimodal Transformer Distillation for Audio-Visual Synchronization

Audio-visual synchronization aims to determine whether the mouth movements and speech in the video are synchronized. VocaLiST reaches state-of-the-art performance by incorporating multimodal Transformers to model audio-visual interact information. However, it requires high computing resources, making it impractical for real-world applications. This paper proposed an MTDVocaLiST model, which is trained by our proposed multimodal Transformer distillation (MTD) loss. MTD loss enables MTDVocaLiST model to deeply mimic the cross-attention distribution and value-relation in the Transformer of VocaLiST. Our proposed method is effective in two aspects: From the distillation method perspective, MTD loss outperforms other strong distillation baselines. From the distilled model's performance perspective: 1) MTDVocaLiST outperforms similar-size SOTA models, SyncNet, and PM models by 15.69% and 3.39%; 2) MTDVocaLiST reduces the model size of VocaLiST by 83.52%, yet still maintaining similar performance.

Training strategy for a lightweight countermeasure model for automatic speaker verification

The countermeasure (CM) model is developed to protect Automatic Speaker Verification (ASV) systems from spoof attacks and prevent resulting personal information leakage. Based on practicality and security considerations, the CM model is usually deployed on edge devices, which have more limited computing resources and storage space than cloud-based systems. This work proposes training strategies for a lightweight CM model for ASV, using generalized end-to-end (GE2E) pre-training and adversarial fine-tuning to improve performance, and applying knowledge distillation (KD) to reduce the size of the CM model. In the evaluation phase of the ASVspoof 2021 Logical Access task, the lightweight ResNetSE model reaches min t-DCF 0.2695 and EER 3.54%. Compared to the teacher model, the lightweight student model only uses 22.5% of parameters and 21.1% of multiply and accumulate operands of the teacher model.