Learning to Behave Like Clean Speech: Dual-Branch Knowledge Distillation for Noise-Robust Fake Audio Detection

Most research in fake audio detection (FAD) focuses on improving performance on standard noise-free datasets. However, in actual situations, there is usually noise interference, which will cause significant performance degradation in FAD systems. To improve the noise robustness, we propose a dual-branch knowledge distillation fake audio detection (DKDFAD) method. Specifically, a parallel data flow of the clean teacher branch and the noisy student branch is designed, and interactive fusion and response-based teacher-student paradigms are proposed to guide the training of noisy data from the data distribution and decision-making perspectives. In the noise branch, speech enhancement is first introduced for denoising, which reduces the interference of strong noise. The proposed interactive fusion combines denoising features and noise features to reduce the impact of speech distortion and seek consistency with the data distribution of clean branch. The teacher-student paradigm maps the student's decision space to the teacher's decision space, making noisy speech behave as clean. In addition, a joint training method is used to optimize the two branches to achieve global optimality. Experimental results based on multiple datasets show that the proposed method performs well in noisy environments and maintains performance in cross-dataset experiments

An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation

Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources. Traditionally, these tasks have been tackled using signal processing and machine learning techniques applied to the available acoustic signals. Since the visual aspect of speech is essentially unaffected by the acoustic environment, visual information from the target speakers, such as lip movements and facial expressions, has also been used for speech enhancement and speech separation systems. In order to efficiently fuse acoustic and visual information, researchers have exploited the flexibility of data-driven approaches, specifically deep learning, achieving strong performance. The ceaseless proposal of a large number of techniques to extract features and fuse multimodal information has highlighted the need for an overview that comprehensively describes and discusses audio-visual speech enhancement and separation based on deep learning. In this paper, we provide a systematic survey of this research topic, focusing on the main elements that characterise the systems in the literature: acoustic features; visual features; deep learning methods; fusion techniques; training targets and objective functions. In addition, we review deep-learning-based methods for speech reconstruction from silent videos and audio-visual sound source separation for non-speech signals, since these methods can be more or less directly applied to audio-visual speech enhancement and separation. Finally, we survey commonly employed audio-visual speech datasets, given their central role in the development of data-driven approaches, and evaluation methods, because they are generally used to compare different systems and determine their performance

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation

This paper explores the use of ASR-pretrained Conformers for speaker verification, leveraging their strengths in modeling speech signals. We introduce three strategies: (1) Transfer learning to initialize the speaker embedding network, improving generalization and reducing overfitting. (2) Knowledge distillation to train a more flexible speaker verification model, incorporating frame-level ASR loss as an auxiliary task. (3) A lightweight speaker adaptor for efficient feature conversion without altering the original ASR Conformer, allowing parallel ASR and speaker verification. Experiments on VoxCeleb show significant improvements: transfer learning yields a 0.48% EER, knowledge distillation results in a 0.43% EER, and the speaker adaptor approach, with just an added 4.92M parameters to a 130.94M-parameter model, achieves a 0.57% EER. Overall, our methods effectively transfer ASR capabilities to speaker verification tasks

AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation

Direct speech-to-speech translation (S2ST) aims to convert speech from one language into another, and has demonstrated significant progress to date. Despite the recent success, current S2ST models still suffer from distinct degradation in noisy environments and fail to translate visual speech (i.e., the movement of lips and teeth). In this work, we present AV-TranSpeech, the first audio-visual speech-to-speech (AV-S2ST) translation model without relying on intermediate text. AV-TranSpeech complements the audio stream with visual information to promote system robustness and opens up a host of practical applications: dictation or dubbing archival films. To mitigate the data scarcity with limited parallel AV-S2ST data, we 1) explore self-supervised pre-training with unlabeled audio-visual data to learn contextual representation, and 2) introduce cross-modal distillation with S2ST models trained on the audio-only corpus to further reduce the requirements of visual data. Experimental results on two language pairs demonstrate that AV-TranSpeech outperforms audio-only models under all settings regardless of the type of noise. With low-resource audio-visual data (10h, 30h), cross-modal distillation yields an improvement of 7.6 BLEU on average compared with baselines. Audio samples are available at https://AV-TranSpeech.github.ioComment: Accepted to ACL 202

비화자 요소에 강인한 화자 인식을 위한 딥러닝 기반 성문 추출

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2021. 2. 김남수.Over the recent years, various deep learning-based embedding methods have been proposed and have shown impressive performance in speaker verification. However, as in most of the classical embedding techniques, the deep learning-based methods are known to suffer from severe performance degradation when dealing with speech samples with different conditions (e.g., recording devices, emotional states). Also, unlike the classical Gaussian mixture model (GMM)-based techniques (e.g., GMM supervector or i-vector), since the deep learning-based embedding systems are trained in a fully supervised manner, it is impossible for them to utilize unlabeled dataset when training. In this thesis, we propose a variational autoencoder (VAE)-based embedding framework, which extracts the total variability embedding and a representation for the uncertainty within the input speech distribution. Unlike the conventional deep learning-based embedding techniques (e.g., d-vector or x-vector), the proposed VAE-based embedding system is trained in an unsupervised manner, which enables the utilization of unlabeled datasets. Furthermore, in order to prevent the potential loss of information caused by the Kullback-Leibler divergence regularization term in the VAE-based embedding framework, we propose an adversarially learned inference (ALI)-based embedding technique. Both VAE- and ALI-based embedding techniques have shown great performance in terms of short duration speaker verification, outperforming the conventional i-vector framework. Additionally, we present a fully supervised training method for disentangling the non-speaker nuisance information from the speaker embedding. The proposed training scheme jointly extracts the speaker and nuisance attribute (e.g., recording channel, emotion) embeddings, and train them to have maximum information on their main-task while ensuring maximum uncertainty on their sub-task. Since the proposed method does not require any heuristic training strategy as in the conventional disentanglement techniques (e.g., adversarial learning, gradient reversal), optimizing the embedding network is relatively more stable. The proposed scheme have shown state-of-the-art performance in RSR2015 Part 3 dataset, and demonstrated its capability in efficiently disentangling the recording device and emotional information from the speaker embedding.최근 몇년간 다양한 딥러닝 기반 성문 추출 기법들이 제안되어 왔으며, 화자 인식에서 높은 성능을 보였다. 하지만 고전적인 성문 추출 기법에서와 마찬가지로, 딥러닝 기반 성문 추출 기법들은 서로 다른 환경 (e.g., 녹음 기기, 감정)에서 녹음된 음성들을 분석하는 과정에서 성능 저하를 겪는다. 또한 기존의 가우시안 혼합 모델 (Gaussian mixture model, GMM) 기반의 기법들 (e.g., GMM 슈퍼벡터, i-벡터)와 달리 딥러닝 기반 성문 추출 기법들은 교사 학습을 통하여 최적화되기에 라벨이 없는 데이터를 활용할 수 없다는 한계가 있다. 본 논문에서는 variational autoencoder (VAE) 기반의 성문 추출 기법을 제안하며, 해당 기법에서는 음성 분포 패턴을 요약하는 벡터와 음성 내의 불확실성을 표현하는 벡터를 추출한다. 기존의 딥러닝 기반 성문 추출 기법 (e.g., d-벡터, x-벡터)와는 달리, 제안하는 기법은 비교사 학습을 통하여 최적화 되기에 라벨이 없는 데이터를 활용할 수 있다. 더 나아가 VAE의 KL-divergence 제약 함수로 인한 정보 손실을 방지하기 위하여 adversarially learned inference (ALI) 기반의 성문 추출 기법을 추가적으로 제안한다. 제안한 VAE 및 ALI 기반의 성문 추출 기법은 짧은 음성에서의 화자 인증 실험에서 높은 성능을 보였으며, 기존의 i-벡터 기반의 기법보다 좋은 결과를 보였다. 또한 본 논문에서는 성문 벡터로부터 비 화자 요소 (e.g., 녹음 기기, 감정)에 대한 정보를 제거하는 학습법을 제안한다. 제안하는 기법은 화자 벡터와 비화자 벡터를 동시에 추출하며, 각 벡터는 자신의 주 목적에 대한 정보를 최대한 많이 유지하되, 부 목적에 대한 정보를 최소화하도록 학습된다. 기존의 비 화자 요소 정보 제거 기법들 (e.g., adversarial learning, gradient reversal)에 비하여 제안하는 기법은 휴리스틱한 학습 전략을 요하지 않기에, 보다 안정적인 학습이 가능하다. 제안하는 기법은 RSR2015 Part3 데이터셋에서 기존 기법들에 비하여 높은 성능을 보였으며, 성문 벡터 내의 녹음 기기 및 감정 정보를 억제하는데 효과적이었다.1. Introduction 1 2. Conventional embedding techniques for speaker recognition 7 2.1. i-vector framework 7 2.2. Deep learning-based speaker embedding 10 2.2.1. Deep embedding network 10 2.2.2. Conventional disentanglement methods 13 3. Unsupervised learning of total variability embedding for speaker verification with random digit strings 17 3.1. Introduction 17 3.2. Variational autoencoder 20 3.3. Variational inference model for non-linear total variability embedding 22 3.3.1. Maximum likelihood training 23 3.3.2. Non-linear feature extraction and speaker verification 25 3.4. Experiments 26 3.4.1. Databases 26 3.4.2. Experimental setup 27 3.4.3. Effect of the duration on the latent variable 28 3.4.4. Experiments with VAEs 30 3.4.5. Feature-level fusion of i-vector and latent variable 33 3.4.6. Score-level fusion of i-vector and latent variable 36 3.5. Summary 39 4. Adversarially learned total variability embedding for speaker recognition with random digit strings 41 4.1. Introduction 41 4.2. Adversarially learned inference 43 4.3. Adversarially learned feature extraction 45 4.3.1. Maximum likelihood criterion 47 4.3.2. Adversarially learned inference for non-linear i-vector extraction 49 4.3.3. Relationship to the VAE-based feature extractor 50 4.4. Experiments 51 4.4.1. Databases 51 4.4.2. Experimental setup 53 4.4.3. Effect of the duration on the latent variable 54 4.4.4. Speaker verification and identification with different utterance-level features 56 4.5. Summary 62 5. Disentangled speaker and nuisance attribute embedding for robust speaker verification 63 5.1. Introduction 63 5.2. Joint factor embedding 67 5.2.1. Joint factor embedding network architecture 67 5.2.2. Training for joint factor embedding 69 5.3. Experiments 71 5.3.1. Channel disentanglement experiments 71 5.3.2. Emotion disentanglement 82 5.3.3. Noise disentanglement 86 5.4. Summary 87 6. Conclusion 93 Bibliography 95 Abstract (Korean) 105Docto