It is known that the performance of the i-vectors/PLDA based speaker verification systems is affected in the cases of short utterances and limited training data The performance degradation appears because the shorter the utterance, the less reliable the extracted i-vector is, and because the total variability covariance matrix and the underlying PLDA matrices need a significant amount of data to be robustly estimated Considering the “MIT Mobile Device Speaker Verification Corpus” (MIT-MDSVC) as a representative dataset for robust speaker verification tasks on limited amount of training data, this paper investigates which configuration and which parameters lead to the best performance of an i-vectors/PLDA based speaker verification The i-vectors/PLDA based system achieved good performance only when the total variability matrix and the underlying PLDA matrices were trained with data belonging to the enrolled speakers This way of training means that the system should be fully retrained when new enrolled speakers were added The performance of the system was more sensitive to the amount of training data of the underlying PLDA matrices than to the amount of training data of the total variability matrix Overall, the Equal Error Rate performance of the i-vectors/PLDA based system was around 1% below the performance of a GMM-UBM system on the chosen dataset The paper presents at the end some preliminary experiments in which the utterances comprised in the CSTR VCTK corpus were used besides utterances from MIT-MDSVC for training the total variability covariance matrix and the underlying PLDA matrices

Front-End Factor Analysis For Speaker Verification

Over the past decades, a tremendous amount of research has been done on the use of machine learning for speech processing applications, especially speech recognition. However, in the past few years, research has focused on utilizing deep learning for speech-related applications. This new area of machine learning has yielded far better results when compared to others in a variety of applications including speech, and thus became a very attractive area of research. This paper provides a thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications. A thorough statistical analysis is provided in this review which was conducted by extracting specific information from 174 papers published between the years 2006 and 2018. The results provided in this paper shed light on the trends of research in this area as well as bring focus to new research topics.

/pdf/speech-recognition-using-deep-neural-networks-a-systematic-kp43ilkz5h.pdf

Speech Recognition Using Deep Neural Networks: A Systematic Review

Experiments in hearing

Current speaker verification techniques rely on a neural network to extract speaker representations. The successful x-vector architecture is a Time Delay Neural Network (TDNN) that applies statistics pooling to project variable-length utterances into fixed-length speaker characterizing embeddings. In this paper, we propose multiple enhancements to this architecture based on recent trends in the related fields of face verification and computer vision. Firstly, the initial frame layers can be restructured into 1-dimensional Res2Net modules with impactful skip connections. Similarly to SE-ResNet, we introduce Squeeze-and-Excitation blocks in these modules to explicitly model channel interdependencies. The SE block expands the temporal context of the frame layer by rescaling the channels according to global properties of the recording. Secondly, neural networks are known to learn hierarchical features, with each layer operating on a different level of complexity. To leverage this complementary information, we aggregate and propagate features of different hierarchical levels. Finally, we improve the statistics pooling module with channel-dependent frame attention. This enables the network to focus on different subsets of frames during each of the channel’s statistics estimation. The proposed ECAPA-TDNN architecture significantly outperforms state-of-the-art TDNN based systems on the VoxCeleb test sets and the 2019 VoxCeleb Speaker Recognition Challenge.

/pdf/ecapa-tdnn-emphasized-channel-attention-propagation-and-17pe2uxfv9.pdf

ECAPA-TDNN : Emphasized Channel Attention, Propagation and Aggregation in TDNN based speaker verification

Deep learning is progressively gaining popularity as a viable alternative to i-vectors for speaker recognition. Promising results have been recently obtained with Convolutional Neural Networks (CNNs) when fed by raw speech samples directly. Rather than employing standard hand-crafted features, the latter CNNs learn low-level speech representations from waveforms, potentially allowing the network to better capture important narrow-band speaker characteristics such as pitch and formants. Proper design of the neural network is crucial to achieve this goal.This paper proposes a novel CNN architecture, called SincNet, that encourages the first convolutional layer to discover more meaningful filters. SincNet is based on parametrized sinc functions, which implement band-pass filters. In contrast to standard CNNs, that learn all elements of each filter, only low and high cutoff frequencies are directly learned from data with the proposed method. This offers a very compact and efficient way to derive a customized filter bank specifically tuned for the desired application.Our experiments, conducted on both speaker identification and speaker verification tasks, show that the proposed architecture converges faster and performs better than a standard CNN on raw waveforms.

Speaker Recognition from Raw Waveform with SincNet

The duration of speech segments has traditionally been controlled in the NIST speaker recognition evaluations so that researchers working in this framework have been relieved of the responsibility of dealing with the duration variability that arises in practical applications. The fixed dimensional i-vector representation of speech utterances is ideal for working under such controlled conditions and ignoring the fact that i-vectors extracted from short utterances are less reliable than those extracted from long utterances leads to a very simple formulation of the speaker recognition problem. However a more realistic approach seems to be needed to handle duration variability properly. In this paper, we show how to quantify the uncertainty associated with the i-vector extraction process and propagate it into a PLDA classifier. We evaluated this approach using test sets derived from the NIST 2010 core and extended core conditions by randomly truncating the utterances in the female, telephone speech trials so that the durations of all enrollment and test utterances lay in the range 3-60 seconds and we found that it led to substantial improvements in accuracy. Although the likelihood ratio computation for speaker verification is more computationally expensive than in the standard i-vector/PLDA classifier, it is still quite modest as it reduces to computing the probability density functions of two full covariance Gaussians (irrespective of the number of the number of utterances used to enroll a speaker).

/pdf/plda-for-speaker-verification-with-utterances-of-arbitrary-akopktx4q3.pdf

PLDA for speaker verification with utterances of arbitrary duration

The performance of a state-of-the-art speaker verification system is severely degraded when it is presented with trial recordings of short duration. In this work we propose to use deep neural networks to learn short-duration speaker embeddings. We focus on the 5s-5s condition, wherein both sides of a verification trial are 5 seconds long. In our previous work we established that learning a non-linear mapping from i-vectors to speaker labels is beneficial for speaker verification [1]. In this work we take the idea of learning a speaker classifier one step further we apply deep neural networks directly to timefrequency speech representations. We propose two feedforward network architectures for this task. Our best model is based on a deep convolutional architecture wherein recordings are treated as images. From our experimental findings we advocate treating utterances as images or ‘speaker snapshots, much like in face recognition. Our convolutional speaker embeddings perform significantly better than i-vectors when scoring is done using cosine distance, where the relative improvement is 23.5%. The proposed deep embeddings combined with cosine distance also outperform a state-of-the-art i-vector verification system by 1%, providing further empirical evidence in favor of our learned speaker features.

Deep Speaker Embeddings for Short-Duration Speaker Verification.

http://cs.joensuu.fi/pages/tkinnu/webpage/pdf/multitaper_speechcomm.pdf

Multitaper MFCC and PLP features for speaker verification using i-vectors

The 2016 edition of the NIST speaker recognition evaluation tests the ability of speaker verification systems to deal with domain mismatch between development and test data. In order to adapt to new languages, a small amount of unlabeled, in-domain data was provided warranting the need for an unsupervised approach to learn from this data. In this work we adapt a simple domain adaptation strategy to the speaker verification problem. We test our approach using two types of speaker embeddings i-vectors and neural network based x-vectors. Despite the simplicity of our method, we show that it outperforms a competitive PLDA domain-adaptation approach in the i-vector domain (12.11% vs 12.68% EER), and works as well in the x-vector domain (8.93% vs 8.91% EER) when the dimension of x-vectors is reduced to 150 by using LDA. Finally, as our approach adapts the speaker embeddings, we combined our adapted embeddings with the PLDA adaptation approach. We achieved our best result (8.23% EER) using this strategy with x-vector embeddings.

Speaker Verification in Mismatched Conditions with Frustratingly Easy Domain Adaptation.

This article presents a novel approach for learning domain-invariant speaker embeddings using Generative Adversarial Networks. The main idea is to confuse a domain discriminator so that it cannot tell if embeddings are from the source or target domains. We train several GAN variants using our proposed framework and apply them to the speaker verification task. On the challenging NIST-SRE 2016 dataset, we are able to match the performance of a strong baseline x-vector system. In contrast to the the baseline systems which are dependent on dimensionality reduction (LDA) and an external classifier (PLDA), our proposed speaker embeddings can be scored using simple cosine distance. This is achieved by optimizing our models end-to-end, using an angular margin loss function. Furthermore, we are able to significantly boost verification performance by averaging our different GAN models at the score level, achieving a relative improvement of 7.2% over the baseline.

Jahangir Alam

Papers

PLDA for speaker verification with utterances of arbitrary duration

Deep Speaker Embeddings for Short-Duration Speaker Verification.

Multitaper MFCC and PLP features for speaker verification using i-vectors

Speaker Verification in Mismatched Conditions with Frustratingly Easy Domain Adaptation.

Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-end Speaker Verification