A variational EM algorithm for learning eigenvoice parameters in mixed signals

We derive an efficient learning algorithm for model-based source separation for use on single channel speech mixtures where the precise source characteristics are not known a priori. The sources are modeled using factor-analyzed hidden Markov models (HMM) where source specific characteristics are captured by an "eigenvoice" speaker subspace model. The proposed algorithm is able to learn adaptation parameters for two speech sources when only a mixture of signals is observed. We evaluate the algorithm on the 2006 speech separation challenge data set and show that it is significantly faster than our earlier system at a small cost in terms of performance

Combining Localization Cues and Source Model Constraints for Binaural Source Separation

We describe a system for separating multiple sources from a two-channel recording based on interaural cues and prior knowledge of the statistics of the underlying source signals. The proposed algorithm effectively combines information derived from low level perceptual cues, similar to those used by the human auditory system, with higher level information related to speaker identity. We combine a probabilistic model of the observed interaural level and phase differences with a prior model of the source statistics and derive an EM algorithm for finding the maximum likelihood parameters of the joint model. The system is able to separate more sound sources than there are observed channels in the presence of reverberation. In simulated mixtures of speech from two and three speakers the proposed algorithm gives a signal-to-noise ratio improvement of 1.7 dB over a baseline algorithm which uses only interaural cues. Further improvement is obtained by incorporating eigenvoice speaker adaptation to enable the source model to better match the sources present in the signal. This improves performance over the baseline by 2.7 dB when the speakers used for training and testing are matched. However, the improvement is minimal when the test data is very different from that used in training

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

An Experimental Review of Speaker Diarization methods with application to Two-Speaker Conversational Telephone Speech recordings

We performed an experimental review of current diarization systems for the conversational telephone speech (CTS) domain. In detail, we considered a total of eight different algorithms belonging to clustering-based, end-to-end neural diarization (EEND), and speech separation guided diarization (SSGD) paradigms. We studied the inference-time computational requirements and diarization accuracy on four CTS datasets with different characteristics and languages. We found that, among all methods considered, EEND-vector clustering (EEND-VC) offers the best trade-off in terms of computing requirements and performance. More in general, EEND models have been found to be lighter and faster in inference compared to clustering-based methods. However, they also require a large amount of diarization-oriented annotated data. In particular EEND-VC performance in our experiments degraded when the dataset size was reduced, whereas self-attentive EEND (SA-EEND) was less affected. We also found that SA-EEND gives less consistent results among all the datasets compared to EEND-VC, with its performance degrading on long conversations with high speech sparsity. Clustering-based diarization systems, and in particular VBx, instead have more consistent performance compared to SA-EEND but are outperformed by EEND-VC. The gap with respect to this latter is reduced when overlap-aware clustering methods are considered. SSGD is the most computationally demanding method, but it could be convenient if speech recognition has to be performed. Its performance is close to SA-EEND but degrades significantly when the training and inference data characteristics are less matched.Comment: 52 pages, 10 figure

Single channel audio separation using deep neural networks and matrix factorizations

PhD ThesisSource Separation has become a significant research topic in the signal processing community and the machine learning area. Due to numerous applications, such as automatic speech recognition and speech communication, separation of target speech from the mixed signal is of great importance. In many practical applications, speech separation from a single recorder is most desirable from an application standpoint. In this thesis, two novel approaches have been proposed to address this single channel audio separation problem. This thesis first reviews traditional approaches for single channel source separation, and later elicits a generic approach, which is more capable of feature learning, i.e. deep graphical models. In the first part of this thesis, a novel approach based on matrix factorization and hierarchical model has been proposed. In this work, an artificial stereo mixture is formulated to provide extra information. In addition, a hybrid framework that combines the generalized Expectation-Maximization algorithm with a multiplicative update rule is proposed to optimize the parameters of a matrix factorization based approach to approximatively separate the mixture. Furthermore, a hierarchical model based on an extreme learning machine is developed to check the validity of the approximately separated sources followed by an energy minimization method to further improve the quality of the separated sources by generating a time-frequency mask. Various experiments have been conducted and the obtained results have shown that the proposed approach outperforms conventional approaches not only in reduction of computational complexity, but also the separation performance. In the second part, a deep neural network based ensemble system is proposed. In this work, the complementary property of different features are fully explored by ‘wide’ and ‘forward’ ensemble system. In addition, instead of using the features learned from the output layer, the features learned from the penultimate layer are investigated. The final embedded features are classified with an extreme learning machine to generate a binary mask to separate a mixed signal. The experiment focuses on speech in the presence of music and the obtained results demonstrated that the proposed ensemble system has the ability to explore the complementary property of various features thoroughly under various conditions with promising separation performance

Anti-Spoofing for Text-Independent Speaker Verification: An Initial Database, Comparison of Countermeasures, and Human Performance

Due to copyright restrictions, the access to the full text of this article is only available via subscription.In this paper, we present a systematic study of the vulnerability of automatic speaker verification to a diverse range of spoofing attacks. We start with a thorough analysis of the spoofing effects of five speech synthesis and eight voice conversion systems, and the vulnerability of three speaker verification systems under those attacks. We then introduce a number of countermeasures to prevent spoofing attacks from both known and unknown attackers. Known attackers are spoofing systems whose output was used to train the countermeasures, while an unknown attacker is a spoofing system whose output was not available to the countermeasures during training. Finally, we benchmark automatic systems against human performance on both speaker verification and spoofing detection tasks.EPSRC ; TÜBİTA