Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability space because it models both speaker and channel variabilities. Two speaker verification systems are proposed which use this new representation. The first system is a support vector machine-based system that uses the cosine kernel to estimate the similarity between the input data. The second system directly uses the cosine similarity as the final decision score. We tested three channel compensation techniques in the total variability space, which are within-class covariance normalization (WCCN), linear discriminate analysis (LDA), and nuisance attribute projection (NAP). We found that the best results are obtained when LDA is followed by WCCN. We achieved an equal error rate (EER) of 1.12% and MinDCF of 0.0094 using the cosine distance scoring on the male English trials of the core condition of the NIST 2008 Speaker Recognition Evaluation dataset. We also obtained 4% absolute EER improvement for both-gender trials on the 10 s-10 s condition compared to the classical joint factor analysis scoring.

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Front-End Factor Analysis for Speaker Verification

National Institute of Standards and Technology における超伝導研究及び生活

In this paper, we use data augmentation to improve performance of deep neural network (DNN) embeddings for speaker recognition. The DNN, which is trained to discriminate between speakers, maps variable-length utterances to fixed-dimensional embeddings that we call x-vectors. Prior studies have found that embeddings leverage large-scale training datasets better than i-vectors. However, it can be challenging to collect substantial quantities of labeled data for training. We use data augmentation, consisting of added noise and reverberation, as an inexpensive method to multiply the amount of training data and improve robustness. The x-vectors are compared with i-vector baselines on Speakers in the Wild and NIST SRE 2016 Cantonese. We find that while augmentation is beneficial in the PLDA classifier, it is not helpful in the i-vector extractor. However, the x-vector DNN effectively exploits data augmentation, due to its supervised training. As a result, the x-vectors achieve superior performance on the evaluation datasets.

X-Vectors: Robust DNN Embeddings for Speaker Recognition

Digital processing of speech signals

We propose a novel framework for speaker recognition in which extraction of sufficient statistics for the state-of-the-art i-vector model is driven by a deep neural network (DNN) trained for automatic speech recognition (ASR) Specifically, the DNN replaces the standard Gaussian mixture model (GMM) to produce frame alignments The use of an ASR-DNN system in the speaker recognition pipeline is attractive as it integrates the information from speech content directly into the statistics, allowing the standard backends to remain unchanged Improvement from the proposed framework compared to a state-of-the-art system are of 30% relative at the equal error rate when evaluated on the telephone conditions from the 2012 NIST speaker recognition evaluation (SRE) The proposed framework is a successful way to efficiently leverage transcribed data for speaker recognition, thus opening up a wide spectrum of research directions

A novel scheme for speaker recognition using a phonetically-aware deep neural network

The Speakers in the Wild (SITW) speaker recognition database contains hand-annotated speech samples from open-source media for the purpose of benchmarking text-independent speaker recognition technology on single and multi-speaker audio acquired across unconstrained or “wild” conditions. The database consists of recordings of 299 speakers, with an average of eight different sessions per person. Unlike existing databases for speaker recognition, this data was not collected under controlled conditions and thus contains real noise, reverberation, intraspeaker variability and compression artifacts. These factors are often convolved in the real world, as the SITW data shows, and they make SITW a challenging database for singleand multispeaker recognition

The Speakers in the Wild (SITW) Speaker Recognition Database.

The recent application of deep neural networks (DNN) to speaker identification (SID) has resulted in significant improvements over current state-of-the-art on telephone speech. In this work, we report a similar achievement in DNN-based SID performance on microphone speech. We consider two approaches to DNN-based SID: one that uses the DNN to extract features, and another that uses the DNN during feature modeling. Modeling is conducted using the DNN/i-vector framework, in which the traditional universal background model is replaced with a DNN. The recently proposed use of bottleneck features extracted from a DNN is also evaluated. Systems are first compared with a conventional universal background model (UBM) Gaussian mixture model (GMM) i-vector system on the clean conditions of the NIST 2012 speaker recognition evaluation corpus, where a lack of robustness to microphone speech is found. Several methods of DNN feature processing are then applied to bring significantly greater robustness to microphone speech. To direct future research, the DNN-based systems are also evaluated in the context of audio degradations including noise and reverberation.

Advances in deep neural network approaches to speaker recognition

The recent development of the i-vector framework for speaker recognition has set a new performance standard in the research field. An i-vector is a compact representation of a speakers utterance extracted from a total variability subspace. Prior to classification using a cosine kernel, i-vectors are projected into an linear discriminant analysis (LDA) space in order to reduce inter-session variability and enhance speaker discrimination. The accurate estimation of this LDA space from a training dataset is crucial to detection performance. A typical training dataset, however, does not consist of utterances acquired through all sources of interest for each speaker. This has the effect of introducing systematic variation related to the speech source in the between-speaker covariance matrix and results in an incomplete representation of the within-speaker scatter matrix used for LDA. The recently proposed source-normalized (SN) LDA algorithm improves the robustness of i-vector-based speaker recognition under both mis-matched evaluation conditions and conditions for which inadequate speech resources are available for suitable system development. When evaluated on the recent NIST 2008 and 2010 Speaker Recognition Evaluations (SRE), SN-LDA demonstrated relative improvements of up to 38% in equal error rate (EER) and 44% in minimum DCF over LDA under mis-matched and sparsely resourced evaluation conditions while also providing improvements in the common telephone-only conditions. Extending on these initial developments, this study provides a thorough analysis of how SN-LDA transforms the i-vector space to reduce source variation and its robustness to varying evaluation and LDA training conditions. The concept of source-normalization is further extended to within-class covariance normalization (WCCN) and data-driven source detection.

Source-Normalized LDA for Robust Speaker Recognition Using i-Vectors From Multiple Speech Sources

This paper compares different approaches for using deep neural networks (DNNs) trained to predict senone posteriors for the task of spoken language recognition (SLR). These approaches have recently been found to outperform various baseline systems on different datasets, but they have not yet been compared to each other or to a common baseline. Two of these approaches use the DNNs to generate feature vectors which are then processed in different ways to predict the score of each language given a test sample. The features are extracted either from a bottleneck layer in the DNN or from the output layer. In the third approach, the standard i-vector extraction procedure is modified to use the senones as classes and the DNN to predict the zeroth order statistics. We compare these three approaches and conclude that the approach based on bottleneck features followed by i-vector modeling outperform the other two approaches. We also show that score-level fusion of some of these approaches leads to gains over using a single approach for short-duration test samples. Finally, we demonstrate that fusing systems that use DNNs trained with several languages leads to improvements in performance over the best single system, and we propose an adaptation procedure for DNNs trained with languages with less available data. Overall, we show improvements between 40% and 70% relative to a state-of-the-art Gaussian mixture model (GMM) i-vector system on test durations from 3 seconds to 120 seconds on two significantly different tasks: the NIST 2009 language recognition evaluation task and the DARPA RATS language identification task.

Mitchell McLaren

Papers

A novel scheme for speaker recognition using a phonetically-aware deep neural network

The Speakers in the Wild (SITW) Speaker Recognition Database.

Advances in deep neural network approaches to speaker recognition

Source-Normalized LDA for Robust Speaker Recognition Using i-Vectors From Multiple Speech Sources

Study of senone-based deep neural network approaches for spoken language recognition