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.Comment: ASVspoof202

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.Comment: Submitted to ICASSP 202

WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categories

Our research focuses on solving the zero-shot text classification problem in NLP, with a particular emphasis on innovative self-training strategies. To achieve this objective, we propose a novel self-training strategy that uses labels rather than text for training, significantly reducing the model's training time. Specifically, we use categories from Wikipedia as our training set and leverage the SBERT pre-trained model to establish positive correlations between pairs of categories within the same text, facilitating associative training. For new test datasets, we have improved the original self-training approach, eliminating the need for prior training and testing data from each target dataset. Instead, we adopt Wikipedia as a unified training dataset to better approximate the zero-shot scenario. This modification allows for rapid fine-tuning and inference across different datasets, greatly reducing the time required for self-training. Our experimental results demonstrate that this method can adapt the model to the target dataset within minutes. Compared to other BERT-based transformer models, our approach significantly reduces the amount of training data by training only on labels, not the actual text, and greatly improves training efficiency by utilizing a unified training set. Additionally, our method achieves state-of-the-art results on both the Yahoo Topic and AG News datasets

Neuro-Fuzzy and soft computing : A Computational Approach to Learning and Machine Intelligence

New Jerseyxxvi, 614 p.; app., fig., ind.; 24 c

Input Selection for ANFIS Learning

We present a quick and straightfoward way of input selection for neuro-fuzzy modeling using ANFIS. The method is tested on two real-world problems: the nonlinear regression problem of automobile MPG (miles per gallon) prediction, and the nonlinear system identification using the Box and Jenkins gas furnace data [1]

Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for Noise Cancellation

[[abstract]]©1995 World Scientific-This paper presents an innovative application of adaptive noise cancellation using adaptive neuro-fuzzy inference systems (ANFIS). Under certain weak assumptions, ANFIS can successfully model the nonlinear dynamics of a noise passage, thus a distorted noise can be effectively removed from a measured signal. Simulation results demonstrate the feasibility of the proposed approach[[department]]資訊工程學

Hierarchical Filtering Method for Content-based Music Retrieval via Acoustic Input

[[abstract]]This paper presents an implementation of a content-based music retrieval system that can take a user's acoustic input (8-second clip of singing or humming) via a microphone and then retrieve the intended song from a database containing over 3000 candidate songs. The system, known as Super MBox, demonstrates the feasibility of real-time music retrieval with a high success rate. Super MBox first takes the user's acoustic input from a microphone and converts it into a pitch vector. Then a hierarchical filtering method (HFM) is used to first filter out 80% unlikely candidates and then compare the query input with the remaining 20% candidates in a detailed manner. The output of Super MBox is a ranked song list according to the computed similarity scores. A brief mathematical analysis of the two-step HFM is given in the paper to explain how to derive the optimum parameters of the comparison engine. The proposed HFM and its analysis framework can be directly applied to other multimedia information retrieval systems. We have tested Super MBox extensively and found the top-20 success rate is over 85%, based on a dataset of about singing/humming 2000 clips from people with mediocre singing skills. Our studies demonstrate the feasibility of using Super MBox as a prototype for music search engines over the Internet and/or query engines in digital music libraries.[[fileno]]2030226030028[[department]]資訊工程學