1 Introduction
Automatic speaker verification (ASV) is an important biometric authentication technology and has found a broad range of applications. The current ASV methods can be categorized into two groups: the statistical model approach that has gained the most popularity [1, 2, 3], and the neural model approach that emerged recently but has shown great potential [4, 5, 6].
Perhaps the most famous statistical model is the Gaussian mixture model
universal background model (GMM-UBM) [1]. It factorizes the variance of speech signals by the UBM, and then models individual speakers conditioned on that factorization. More succinct models design subspace structures to improve the statistical strength, including the joint factor analysis model
[2] and the i-vector model [3]. Further improvements were obtained by either discriminative models (e.g., PLDA [7]) or phonetic knowledge transfer (e.g., the DNN-based i-vector model [8, 9]).The neural model approach has also been studied for many years as well, however it was not as popular as the statistical model approach until recently training large-scale neural models became feasible. The initial success was reported by Ehsan et al. on a text-dependent task [4], where frame-level speaker features were extracted from the last hidden layer of a deep neural network (DNN), and utterance-based speaker vectors (‘d-vectors’) were derived by averaging the frame-level features. Learning frame-level speaker features offers many advantages, which paves the way to deeper understanding of speech signals.
Researchers followed Ehsan’s work in two directions. In the first direction, more speech-friendly DNN architectures were designed, with the goal of learning stronger frame-level speaker features while keeping the simple d-vector architecture unchanged [6]. In the second direction, researchers pursue end-to-end solutions which produce utterance-level speaker vectors directly [5, 10, 11, 12]. A representative work in this direction is the x-vector architecture proposed by Snyder et al. [12], which produces the utterance-level speaker vectors (x-vectors) from the first- and second-order statistics of the frame-level features.
For both the d-vector and x-vector architectures, however, there are two potential problems. Firstly, the DNN models involve a parametric classifier (i.e., the last affine layer) during model training. This means that part of the knowledge involved in the training data is used to learn a classifier that will be ultimately thrown away during inference, leading to potential ‘information leak’. Secondly, both models do not regulate the distribution of the derived speaker vectors, either at the frame-level or at the utterance-level. The uncontrolled distribution will degrade the subsequent scoring component, especially the PLDA model that assumes the speaker vectors are Gaussian
[7].To deal with these two problems, we propose a Gaussian-constrained training approach in this paper. This new training approach will (1) discard the parametric classifier to mitigate information leak, and (2) enforce the distribution of the derived speaker vectors to be Gaussian to meet the requirement of the scoring component. Our experiments on two databases demonstrated that the approach can produce more representative and regular speaker vectors, with both the d-vector and x-vector models, which in turn leads to consistent performance improvement.
2 Overview for x-vector and d-vector models
The x-vector model and the d-vector model are two typical neural models adopted by ASV researchers. The architecture of these models are shown in Figure 1, in a comparative way.
For the x-vector model, it consists of three components. The first component is used for frame-level feature learning. The input of this component is a sequence of acoustic features, e.g., Fbanks. After several feed-forward neural networks, it outputs a sequence of frame-level speaker features. The second component is a statistic pooling layer, in which statistics of the frame-level features, e.g., mean and standard deviation, are computed. This statistic pooling maps a variable-length input to a fixed-dimensional vector. The third component is used to produce utterance-level speaker vectors. The output layer of this component is a softmax, in which each output node corresponds to a particular speaker in the training data. The network is trained to discriminate all the speakers in the training data, conditioned on the input utterance. Once the model is well trained, utterance-level speaker vectors, i.e., x-vectors, are read from a layer in the last component, and a scoring model, such as PLDA, will be used to score the trials.
The d-vector model has a similar but simpler architecture. It consists of two components, one is for frame-level feature learning, the same as the first component of the x-vector model, and the other is for frame-level speaker embedding, the same as the third component of the x-vector model. Since the entire architecture is frame-based, a pooling layer is not required. The network is trained to discriminate speakers in the training data, but conditioned on each frame. Once the training is done, utterance-level speaker vectors, i.e., d-vectors, are derived by averaging the frame-level features. Finally, a scoring model such as PLDA will be used to score the trials.
3 Gaussian-constrained training
As mentioned in Section 1, both the d-vector and the x-vector models suffer from (1) information leak and (2) unconstrained distribution of speaker vectors. We propose a Gaussian-constrained training approach to solve these problems. In brief, this approach introduces a Gaussian prior on the output of the embedding layer, which can be formulated as a regularization term in the training objective. Training with this augmented objective will enforce the parameters of the classification layer more predictable, so more parameter-free. Meanwhile, it will urge the model producing more Gaussian speaker vectors, at either the frame-level (for d-vector) or the utterance-level (for x-vector).
For a clear presentation, we shall use the x-vector model to describe the process, whereas the same argument applies to the d-vector model in a straightforward way. Specifically, if all the utterance-level x-vectors in the training set have been derived, the speaker-level x-vector can be simply obtained by averaging all the utterance-level x-vectors belong to that speaker. This is formally written as:
(1) |
where is the set of utterances belonging to speaker ; is the x-vector of utterance ; is the speaker-level x-vector.
Based on the speaker-level x-vectors , each speech utterance can be easily classified as follows:
(2) |
which can be regarded as a non-parametric classifier. If we use this non-parametric classifier to replace the parametric classifier (usually the last affine layer) of the x-vector model in Fig. 1, we reach the full-info training proposed by Li et al. [13].
The model can be trained with the classical cross entropy (CE) criterion, written by:
(3) |
where and are the -th speech utterance and the corresponding ground truth label. Note that the gradients on CE will be fully propagated to the weights of the feature learning component, as the parameters of the classifier (2) are , which are dependent on the feature learning component as well. This means all the knowledge of the data will be exploited to learn the feature component, hence amending the information leak problem.
However, we cannot use a ‘virtual classifier’ parameterized by in practical implementation; instead, we resort to an engineering solution that designs a true parametric classifier, and replaces the parameters by regularly. This train-and-replacement scheme works well in many scenarios [13], but may slow the training or cause fluctuation.
A more elegant approach is to keep the classifier parameters, but introduce a regularization term that encourages the parameters approaching to . For this purpose, the following regularization term is designed:
(4) |
where represent the parameters in the classifier that are associated with the output node corresponding to speaker . With this regularization, the training objective is given by:
where controls the strength of the regularization. Clearly, if is sufficiently large, will converge to the speaker-level x-vector . Moreover, it is clear that the regularization term encourages all the utterance-level x-vectors belonging to speaker to be a Gaussian . Therefore, we name this new training approach as Gaussian-constrained training.
The Gaussian-constrained training possesses several advantages. Firstly, it encourages the parameters of the classifier converging to the speaker-level x-vectors, which equals to removing these parameters gradually. This mitigates the information leak problem, but does not suffer from the unstability of the full-info training. For this reason, Gaussian-constrained training can be regarded as a soft full-info training. Secondly, it encourages the utterance-level x-vectors to be Gaussian, which is a key requirement for many scoring models, particularly PLDA. Thirdly, this approach is flexible. We can choose to control the strength of the regularization, or choose other regularization forms to produce speaker vectors in other forms of distributions.
4 Experiments
4.1 Data
Three datasets were used in our experiments: VoxCeleb, SITW and CSLT-SITW. VoxCeleb was used for model training, while the other two were used for evaluation. More information about these three datasets are presented below.
VoxCeleb: A large-scale free speaker database collected by University of Oxford, UK [14]. The entire database involves two parts: VoxCeleb1 and VoxCeleb2. Since part of speakers are shared by VoxCeleb and SITW, a simple data purging was conducted to remove all the data of the shared speakers. The purged dataset involves utterances from speakers. This dataset was used to train both the d-vector model and the x-vector model, plus the LDA and PLDA model. Data augmentation was applied, where the MUSAN corpus [15] was used to generate noisy utterances and the room impulse responses (RIRS) corpus [16] was used to generate reverberant utterances.
SITW: A standard database used to test ASV performance in real-world conditions [17]. It was collected from open-source media channels, and consists of speech data covering well-known persons. There are two standard datasets for testing: Dev. Core and Eval. Core. We used these two sets to conduct the first evaluation in our experiment. Note that the acoustic condition of SITW is similar to that of the training set VoxCeleb, so this evaluation can be regarded as a condition-matched evaluation.
CSLT-SITW: A small datasets collected by CSLT at Tsinghua University. It consists of speakers, each of which records a couple of Chinese digital strings by several mobiles. Each string contains Chinese digits, and the duration is about - seconds. The scenarios involve laboratory, corridor, street, restaurant, bus and subway. Speakers varied their poses during the recording, and the mobile phones were placed both near and far. There are utterances in total.
4.2 Settings
For a comprehensive comparison, three baseline systems following the Kaldi sitw recipe [18] were built: an i-vector system, an x-vector system and a d-vector system.
For the i-vector system, the acoustic feature involves -dimensional MFCCs plus the log energy, augmented by the first- and second-order derivatives. We also apply cepstral mean normalization (CMN) and the energy-based voice active detection (VAD). The UBM consists of Gaussian components, and the dimensionality of the i-vector space is . LDA is applied to reduce the dimensionality of i-vectors to prior to PLDA scoring.
For the x-vector system, the feature-learning component is a 5-layer time-delay neural network (TDNN). The slicing parameters for the five TD layers are: {-, -, , +, +}, {-, , +}, {-, , +}, {}, {}. The statistic pooling layer computes the mean and standard deviation of the frame-level features from a speech segment. The size of the output layer is , corresponding to the number of speakers in the training set. Once trained, the -dimensional activations of the penultimate hidden layer are read out as an x-vector. This vector is then reduced to a -dimensional vector by LDA, and finally the PLDA model is employed to score the trials. Refer to [19] for more details. In the Gaussian-constrained training, the hyper-parameter is empirically set to .
For the d-vector system, the DNN structure is similar to the x-vector system. The only difference is that the statistic pooling layer in the x-vector model is replaced by a TD layer whose slicing parameter is set to {-, , +}. Once trained, the -dimensional deep speaker features are derived from the output of the penultimate hidden layer, and the utterance-level d-vectors are obtained by average pooling. Similarly, the d-vectors are transformed to -dimensional vectors by LDA, and the PLDA model is employed to score the trails. The hyper-parameter of the Gaussian-constrained training is empirically set to .
4.3 Results
4.3.1 Sitw
The results on the two SITW evaluation sets, Dev. Core and Eval. Core, are reported in Table 1 and Table 2
, respectively. The results are reported in terms of three metrics: the equal error rate (EER), and the minimum of the normalized detection cost function (minDCF) with two settings: one with the prior target probability
set to (DCF()), and the other with set to (DCF()).From these results, it can be observed that the proposed Gaussian-constrained training improves both the x-vector and d-vector systems, in terms of all these three metrics. Furthermore, this approach seems more effective for the x-vector system. A possible reason is that for the d-vector system, the average pooling may corrupt the Gaussian property. Another possible reason is that the frame-level constraint in the d-vector system may lead to unstable parameter update compared to the utterance-level constraint in the x-vector system. Nevertheless, more investigation is required to understand this discrepancy.
Embedding | DCF() | DCF() | EER(%) |
---|---|---|---|
i-vector | 0.4279 | 0.5734 | 4.967 |
d-vector | 0.4875 | 0.6837 | 5.314 |
d-vector + Gauss | 0.4861 | 0.6812 | 5.160 |
x-vector | 0.3025 | 0.4862 | 2.965 |
x-vector + Gauss | 0.2826 | 0.4551 | 2.734 |
Embedding | DCF() | DCF() | EER(%) |
---|---|---|---|
i-vector | 0.4577 | 0.6214 | 5.249 |
d-vector | 0.5206 | 0.7570 | 5.686 |
d-vector + Gauss | 0.5149 | 0.7496 | 5.659 |
x-vector | 0.3235 | 0.4875 | 3.390 |
x-vector + Gauss | 0.3032 | 0.4520 | 3.034 |
4.3.2 Cslt-Sitw
The performance on the CSLT-SITW set is reported in Table 3. Note that the acoustic properties and linguistic conditions are clearly different from the training data. From Table 3, it can be observed that in spite of this mismatch, the Gaussian-constrained training still delivers consistent performance improvement on both the x-vector and d-vector systems, at least in terms of EER and DCF(). The strange degradation in DCF() may be attributed to the fact that this new training approach emphasizes on a different operation point, though more analysis is required.
Embedding | DCF() | DCF() | EER(%) |
---|---|---|---|
i-vector | 0.4425 | 0.5698 | 6.479 |
d-vector | 0.3881 | 0.4584 | 5.494 |
d-vector + Gauss | 0.3706 | 0.4701 | 5.297 |
x-vector | 0.2731 | 0.3227 | 4.139 |
x-vector + Gauss | 0.2418 | 0.3746 | 3.474 |
5 Conclusions
This paper proposed a Gaussian-constrained training that can be applied to both the feature-based system (d-vector) and utterance-based system (x-vector) for ASV. The basic idea is to enforce a parameter-free classifier so that all the knowledge of the training data would be learned by the feature component; additionally, it encourages the derived speaker features, at either frame-level or utterance-level, to be Gaussian. The former allows more effective usage of the training data, and the latter boosts the PLDA scoring. The experimental results demonstrated that the proposed approach can deliver consistent performance improvement, not only on matched data, but also on entirely new data. As for the future work, more comprehensive analysis will be conducted to understand the behavior of the Gaussian-constrained training, e.g., the impact of the constraint on different layers.
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