There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

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

Matrix Factorization Techniques for Recommender Systems

http://cs.uef.fi/sipu/pub/SRE-review-Kinnunen-and-Li.pdf

An overview of text-independent speaker recognition: From features to supervectors

We present a method to boost the performance of probabilistic generative models that work with i-vector representations. The proposed approach deals with the nonGaussian behavior of i-vectors by performing a simple length normalization. This non-linear transformation allows the use of probabilistic models with Gaussian assumptions that yield equivalent performance to that of more complicated systems based on Heavy-Tailed assumptions. Significant performance improvements are demonstrated on the telephone portion of NIST SRE 2010.

Analysis of i-vector Length Normalization in Speaker Recognition Systems.

This paper investigates replacing i-vectors for text-independent speaker verification with embeddings extracted from a feedforward deep neural network. Long-term speaker characteristics are captured in the network by a temporal pooling layer that aggregates over the input speech. This enables the network to be trained to discriminate between speakers from variablelength speech segments. After training, utterances are mapped directly to fixed-dimensional speaker embeddings and pairs of embeddings are scored using a PLDA-based backend. We compare performance with a traditional i-vector baseline on NIST SRE 2010 and 2016. We find that the embeddings outperform i-vectors for short speech segments and are competitive on long duration test conditions. Moreover, the two representations are complementary, and their fusion improves on the baseline at all operating points. Similar systems have recently shown promising results when trained on very large proprietary datasets, but to the best of our knowledge, these are the best results reported for speaker-discriminative neural networks when trained and tested on publicly available corpora.

Deep Neural Network Embeddings for Text-Independent Speaker Verification.

In this study, we investigate an end-to-end text-independent speaker verification system. The architecture consists of a deep neural network that takes a variable length speech segment and maps it to a speaker embedding. The objective function separates same-speaker and different-speaker pairs, and is reused during verification. Similar systems have recently shown promise for text-dependent verification, but we believe that this is unexplored for the text-independent task. We show that given a large number of training speakers, the proposed system outperforms an i-vector baseline in equal error-rate (EER) and at low miss rates. Relative to the baseline, the end-to-end system reduces EER by 13% average and 29% pooled across test conditions. The fused system achieves a reduction of 32% average and 38% pooled.

Deep neural network-based speaker embeddings for end-to-end speaker verification

Recently, deep neural networks that map utterances to fixed-dimensional embeddings have emerged as the state-of-the-art in speaker recognition. Our prior work introduced x-vectors, an embedding that is very effective for both speaker recognition and diarization. This paper combines our previous work and applies it to the problem of speaker recognition on multi-speaker conversations. We measure performance on Speakers in the Wild and report what we believe are the best published error rates on this dataset. Moreover, we find that diarization substantially reduces error rate when there are multiple speakers, while maintaining excellent performance on single-speaker recordings. Finally, we introduce an easily implemented method to remove the domain-sensitive threshold typically used in the clustering stage of a diarization system. The proposed method is more robust to domain shifts, and achieves similar results to those obtained using a well-tuned threshold.

Daniel Garcia-Romero

Papers

X-Vectors: Robust DNN Embeddings for Speaker Recognition

Analysis of i-vector Length Normalization in Speaker Recognition Systems.

Deep Neural Network Embeddings for Text-Independent Speaker Verification.

Deep neural network-based speaker embeddings for end-to-end speaker verification

Speaker Recognition for Multi-speaker Conversations Using X-vectors