Speaker Identification for Swiss German with Spectral and Rhythm Features

We present results of speech rhythm analysis for automatic speaker identification. We expand previous experiments using similar methods for language identification. Features describing the rhythmic properties of salient changes in signal components are extracted and used in an speaker identification task to determine to which extent they are descriptive of speaker variability. We also test the performance of state-of-the-art but simple-to-extract frame-based features. The paper focus is the evaluation on one corpus (swiss german, TEVOID) using support vector machines. Results suggest that the general spectral features can provide very good performance on this dataset, whereas the rhythm features are not as successful in the task, indicating either the lack of suitability for this task or the dataset specificity

Prosodic-Enhanced Siamese Convolutional Neural Networks for Cross-Device Text-Independent Speaker Verification

In this paper a novel cross-device text-independent speaker verification architecture is proposed. Majority of the state-of-the-art deep architectures that are used for speaker verification tasks consider Mel-frequency cepstral coefficients. In contrast, our proposed Siamese convolutional neural network architecture uses Mel-frequency spectrogram coefficients to benefit from the dependency of the adjacent spectro-temporal features. Moreover, although spectro-temporal features have proved to be highly reliable in speaker verification models, they only represent some aspects of short-term acoustic level traits of the speaker's voice. However, the human voice consists of several linguistic levels such as acoustic, lexicon, prosody, and phonetics, that can be utilized in speaker verification models. To compensate for these inherited shortcomings in spectro-temporal features, we propose to enhance the proposed Siamese convolutional neural network architecture by deploying a multilayer perceptron network to incorporate the prosodic, jitter, and shimmer features. The proposed end-to-end verification architecture performs feature extraction and verification simultaneously. This proposed architecture displays significant improvement over classical signal processing approaches and deep algorithms for forensic cross-device speaker verification.Comment: Accepted in 9th IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS 2018

Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters

Data privacy is crucial when dealing with biometric data. Accounting for the latest European data privacy regulation and payment service directive, biometric template protection is essential for any commercial application. Ensuring unlinkability across biometric service operators, irreversibility of leaked encrypted templates, and renewability of e.g., voice models following the i-vector paradigm, biometric voice-based systems are prepared for the latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean and cosine comparators are known to ensure data privacy demands, without loss of discrimination nor calibration performance. Bridging gaps from template protection to speaker recognition, two architectures are proposed for the two-covariance comparator, serving as a generative model in this study. The first architecture preserves privacy of biometric data capture subjects. In the second architecture, model parameters of the comparator are encrypted as well, such that biometric service providers can supply the same comparison modules employing different key pairs to multiple biometric service operators. An experimental proof-of-concept and complexity analysis is carried out on the data from the 2013-2014 NIST i-vector machine learning challenge

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

Effects of language mismatch in automatic forensic voice comparison using deep learning embeddings

In forensic voice comparison the speaker embedding has become widely popular in the last 10 years. Most of the pretrained speaker embeddings are trained on English corpora, because it is easily accessible. Thus, language dependency can be an important factor in automatic forensic voice comparison, especially when the target language is linguistically very different. There are numerous commercial systems available, but their models are mainly trained on a different language (mostly English) than the target language. In the case of a low-resource language, developing a corpus for forensic purposes containing enough speakers to train deep learning models is costly. This study aims to investigate whether a model pre-trained on English corpus can be used on a target low-resource language (here, Hungarian), different from the model is trained on. Also, often multiple samples are not available from the offender (unknown speaker). Therefore, samples are compared pairwise with and without speaker enrollment for suspect (known) speakers. Two corpora are applied that were developed especially for forensic purposes, and a third that is meant for traditional speaker verification. Two deep learning based speaker embedding vector extraction methods are used: the x-vector and ECAPA-TDNN. Speaker verification was evaluated in the likelihood-ratio framework. A comparison is made between the language combinations (modeling, LR calibration, evaluation). The results were evaluated by minCllr and EER metrics. It was found that the model pre-trained on a different language but on a corpus with a huge amount of speakers performs well on samples with language mismatch. The effect of sample durations and speaking styles were also examined. It was found that the longer the duration of the sample in question the better the performance is. Also, there is no real difference if various speaking styles are applied