Speaker recognition by means of Deep Belief Networks

Most state-of-the-art speaker recognition systems are based on Gaussian Mixture Models (GMMs), where a speech segment is represented by a compact representation, referred to as "identity vector" (ivector for short), extracted by means of Factor Analysis. The main advantage of this representation is that the problem of intersession variability is deferred to a second stage, dealing with low-dimensional vectors rather than with the high-dimensional space of the GMM means. In this paper, we propose to use as a pseudo-ivector extractor a Deep Belief Network (DBN) architecture, trained with the utterances of several hundred speakers. In this approach, the DBN performs a non-linear transformation of the input features, which produces the probability that an output unit is on, given the input features. We model the distribution of the output units, given an utterance, by a reduced set of parameters that embed the speaker characteristics. Tested on the dataset exploited for training the systems that have been used for the NIST 2012 Speaker Recognition Evaluation, this approach shows promising result

On the use of i-vector posterior distributions in Probabilistic Linear Discriminant Analysis

The i-vector extraction process is affected by several factors such as the noise level, the acoustic content of the observed features, the channel mismatch between the training conditions and the test data, and the duration of the analyzed speech segment. These factors influence both the i-vector estimate and its uncertainty, represented by the i-vector posterior covariance. This paper presents a new PLDA model that, unlike the standard one, exploits the intrinsic i-vector uncertainty. Since the recognition accuracy is known to decrease for short speech segments, and their length is one of the main factors affecting the i-vector covariance, we designed a set of experiments aiming at comparing the standard and the new PLDA models on short speech cuts of variable duration, randomly extracted from the conversations included in the NIST SRE 2010 extended dataset, both from interviews and telephone conversations. Our results on NIST SRE 2010 evaluation data show that in different conditions the new model outperforms the standard PLDA by more than 10% relative when tested on short segments with duration mismatches, and is able to keep the accuracy of the standard model for long enough speaker segments. This technique has also been successfully tested in the NIST SRE 2012 evaluation

Speaker recognition by means of Deep Belief Networks

Most state–of–the–art speaker recognition systems are based on Gaussian Mixture Models (GMMs), where a speech segment is represented by a compact representation, referred to as “identity vector” (ivector for short), extracted by means of Factor Analysis. The main advantage of this representation is that the problem of intersession variability is deferred to a second stage, dealing with low-dimensional vectors rather than with the high-dimensional space of the GMM means. In this paper, we propose to use as a pseudo-ivector extractor a Deep Belief Network (DBN) architecture, trained with the utterances of several hundred speakers. In this approach, the DBN performs a non-linear transformation of the input features, which produces the probability that an output unit is on, given the input features. We model the distribution of the output units, given an utterance, by a reduced set of parameters that embed the speaker characteristics. Tested on the dataset exploited for training the systems that have been used for the NIST 2012 Speaker Recognition Evaluation, this approach shows promising results

Large scale training of Pairwise Support Vector Machines for speaker recognition

State–of–the–art systems for text–independent speaker recognition use as their features a compact representation of a speaker utterance, known as “i–vector”. We recently presented an efficient approach for training a Pairwise Support Vector Machine (PSVM) with a suitable kernel for i–vector pairs for a quite large speaker recognition task. Rather than estimating an SVM model per speaker, according to the “one versus all” discriminative paradigm, the PSVM approach classifies a trial, consisting of a pair of i–vectors, as belonging or not to the same speaker class. Training a PSVM with large amount of data, however, is a memory and computational expensive task, because the number of training pairs grows quadratically with the number of training i–vectors. This paper demonstrates that a very small subset of the training pairs is necessary to train the original PSVM model, and proposes two approaches that allow discarding most of the training pairs that are not essential, without harming the accuracy of the model. This allows dramatically reducing the memory and computational resources needed for training, which becomes feasible with large datasets including many speakers. We have assessed these approaches on the extended core conditions of the NIST 2012 Speaker Recognition Evaluation. Our results show that the accuracy of the PSVM trained with a sufficient number of speakers is 10-30% better compared to the one obtained by a PLDA model, depending on the testing conditions. Since the PSVM accuracy increases with the training set size, but PSVM training does not scale well for large numbers of speakers, our selection techniques become relevant for training accurate discriminative classifiers

Scoring heterogeneous speaker vectors using nonlinear transformations and tied PLDa models

Most current state-of-the-art text-independent speaker recognition systems are based on i-vectors, and on probabilistic linear discriminant analysis (PLDA). PLDA assumes that the i-vectors of a trial are homogeneous, i.e., that they have been extracted by the same system. In other words, the enrollment and test i-vectors belong to the same class. However, it is sometimes important to score trials including “heterogeneous” i-vectors, for instance, enrollment i-vectors extracted by an old system, and test i-vectors extracted by a newer, more accurate, system. In this paper, we introduce a PLDA model that is able to score heterogeneous i-vectors independent of their extraction approach, dimensions, and any other characteristics that make a set of i-vectors of the same speaker belong to different classes. The new model, which will be referred to as nonlinear tied-PLDA (NL-Tied-PLDA), is obtained by a generalization of our recently proposed nonlinear PLDA approach, which jointly estimates the PLDA parameters and the parameters of a nonlinear transformation of the i-vectors. The generalization consists of estimating a class-dependent nonlinear transformation of the i-vectors, with the constraint that the transformed i-vectors of the same speaker share the same speaker factor. The resulting model is flexible and accurate, as assessed by the results of a set of experiments performed on the extended core NIST SRE 2012 evaluation. In particular, NL-Tied-PLDA provides better results on heterogeneous trials with respect to the corresponding homogeneous trials scored by the old system, and, in some configurations, it also reaches the accuracy of the new system. Similar results were obtained on the female-extended core NIST SRE 2010 telephone condition

Attentive Statistics Pooling for Deep Speaker Embedding

This paper proposes attentive statistics pooling for deep speaker embedding in text-independent speaker verification. In conventional speaker embedding, frame-level features are averaged over all the frames of a single utterance to form an utterance-level feature. Our method utilizes an attention mechanism to give different weights to different frames and generates not only weighted means but also weighted standard deviations. In this way, it can capture long-term variations in speaker characteristics more effectively. An evaluation on the NIST SRE 2012 and the VoxCeleb data sets shows that it reduces equal error rates (EERs) from the conventional method by 7.5% and 8.1%, respectively.Comment: Proc. Interspeech 2018, pp2252--2256. arXiv admin note: text overlap with arXiv:1809.0931

Speaker recognition by means of restricted Boltzmann machine adaptation

Restricted Boltzmann Machines (RBMs) have shown success in speaker recognition. In this paper, RBMs are investigated in a framework comprising a universal model training and model adaptation. Taking advantage of RBM unsupervised learning algorithm, a global model is trained based on all available background data. This general speaker-independent model, referred to as URBM, is further adapted to the data of a specific speaker to build speaker-dependent model. In order to show its effectiveness, we have applied this framework to two different tasks. It has been used to discriminatively model target and impostor spectral features for classification. It has been also utilized to produce a vector-based representation for speakers. This vector-based representation, similar to i-vector, can be further used for speaker recognition using either cosine scoring or Probabilistic Linear Discriminant Analysis (PLDA). The evaluation is performed on the core test condition of the NIST SRE 2006 database.Peer ReviewedPostprint (author's final draft