We propose an independence criterion based on the eigen-spectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

Measuring statistical dependence with Hilbert-Schmidt norms

Speaker recognition based on deep learning: An overview

Speaker recognition is a task of identifying persons from their voices. Recently, deep learning has dramatically revolutionized speaker recognition. However, there is lack of comprehensive reviews on the exciting progress. 
In this paper, we review several major subtasks of speaker recognition, including speaker verification, identification, diarization, and robust speaker recognition, with a focus on deep-learning-based methods. Because the major advantage of deep learning over conventional methods is its representation ability, which is able to produce highly abstract embedding features from utterances, we first pay close attention to deep-learning-based speaker feature extraction, including the inputs, network structures, temporal pooling strategies, and objective functions respectively, which are the fundamental components of many speaker recognition subtasks. Then, we make an overview of speaker diarization, with an emphasis of recent supervised, end-to-end, and online diarization. Finally, we survey robust speaker recognition from the perspectives of domain adaptation and speech enhancement, which are two major approaches of dealing with domain mismatch and noise problems. Popular and recently released corpora are listed at the end of the paper.

Speaker Recognition Based on Deep Learning: An Overview

Time delay neural network (TDNN) has been widely used in speaker verification tasks. Recently, two TDNN-based models, including extended TDNN (E-TDNN) and factorized TDNN (F-TDNN), are proposed to improve the accuracy of vanilla TDNN. But E-TDNN and F-TDNN increase the number of parameters due to deeper networks, compared with vanilla TDNN. In this paper, we propose a novel TDNN-based model, called densely connected TDNN (D-TDNN), by adopting bottleneck layers and dense connectivity. D-TDNN has fewer parameters than existing TDNN-based models. Furthermore, we propose an improved variant of D-TDNN, called D-TDNN-SS, to employ multiple TDNN branches with short-term and longterm contexts. D-TDNN-SS can integrate the information from multiple TDNN branches with a newly designed channel-wise selection mechanism called statistics-and-selection (SS). Experiments on VoxCeleb datasets show that both D-TDNN and D-TDNN-SS can outperform existing models to achieve stateof-the-art accuracy with fewer parameters, and D-TDNN-SS can achieve better accuracy than D-TDNN.

Densely Connected Time Delay Neural Network for Speaker Verification.

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.

Machine Learning for Speaker Recognition

End-to-end speaker embedding systems have shown promising performance on speaker verification tasks. Traditional end-to-end systems typically adopt softmax loss as training criterion, which is not strong enough for training discriminative models. In this paper, we adapt the additive margin softmax (AM-Softmax) loss, which is originally proposed for face verification, to speaker embedding systems. Furthermore, we propose a novel ensemble loss, called ensemble additive margin softmax (EAM-Softmax) loss, for speaker embedding by integrating Hilbert-Schmidt independence criterion (HSIC) into the speaker embedding system with the AM-Softmax loss. Experiments on a large-scale dataset VoxCeleb show that AM-Softmax loss is better than traditional loss functions, and approaches using EAM-Softmax loss can outperform existing speaker verification methods to achieve state-of-the-art performance.

Ensemble Additive Margin Softmax for Speaker Verification

Speaker identification and retrieval have been widely used in real applications. To overcome the inefficiency problem caused by real-valued representations, there have appeared some speaker hashing methods for speaker identification and retrieval by learning binary codes as representations. However, these hashing methods are based on i-vector and cannot achieve satisfactory retrieval accuracy as they cannot learn discriminative feature representations. In this paper, we propose a novel deep hashing method, called deep additive margin hashing (DAMH), to improve retrieval performance for speaker identification and retrieval task. Compared with existing speaker hashing methods, DAMH can perform feature learning and binary code learning seamlessly by incorporating these two procedures into an end-to-end architecture. Experimental results on a large-scale audio dataset VoxCeleb2 show that DAMH can outperform existing speaker hashing methods to achieve state-of-the-art performance.

Deep Hashing for Speaker Identification and Retrieval.

We present a novel framework to efficiently acquire near-planar anisotropic reflectance in a pixel-independent fashion, using a deep gated mixtureof-experts. While existing work employs a unified network to handle all possible input, our network automatically learns to condition on the input for enhanced reconstruction. We train a gating module to select one out of a number of specialized decoders for reflectance reconstruction, based on photometric measurements, essentially trading generality for quality. A common, pre-trained latent transform module is also appended to each decoder, to offset the burden of the increased number of decoders. In addition, the illumination conditions during acquisition can be jointly optimized. The effectiveness of our framework is validated on a wide variety of challenging samples using a near-field lightstage. Compared with the state-of-the-art technique, our results are improved at the same input bandwidth, and our bandwidth can be reduced to about 1/3 for equal-quality results.

pdf/efficient-reflectance-capture-with-a-deep-gated-mixture-of-2cvypwcd.pdf

Efficient Reflectance Capture with a Deep Gated Mixture-of-Experts

Performance degradation caused by noise has been a long-standing challenge for speaker verification. Previous methods usually involve applying a denoising transformation to speaker embeddings or enhancing input features. Nevertheless, these methods are lossy and inefficient for speaker embedding. In this paper, we propose context- aware masking (CAM), a novel method to extract robust speaker embedding. CAM enables the speaker embedding network to "focus" on the speaker of interest and "blur" unrelated noise. The threshold of masking is dynamically controlled by an auxiliary context embedding that captures speaker and noise characteristics. Moreover, models adopting CAM can be trained in an end-to-end manner without using synthesized noisy-clean speech pairs. Our results show that CAM improves speaker verification performance in the wild by a large margin, compared to the baselines.

Cam: Context-Aware Masking for Robust Speaker Verification

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