529 research outputs found

    Factor analysis modelling for speaker verification with short utterances

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    This paper examines combining both relevance MAP and subspace speaker adaptation processes to train GMM speaker models for use in speaker verification systems with a particular focus on short utterance lengths. The subspace speaker adaptation method involves developing a speaker GMM mean supervector as the sum of a speaker-independent prior distribution and a speaker dependent offset constrained to lie within a low-rank subspace, and has been shown to provide improvements in accuracy over ordinary relevance MAP when the amount of training data is limited. It is shown through testing on NIST SRE data that combining the two processes provides speaker models which lead to modest improvements in verification accuracy for limited data situations, in addition to improving the performance of the speaker verification system when a larger amount of available training data is available

    Factorization of Discriminatively Trained i-vector Extractor for Speaker Recognition

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    In this work, we continue in our research on i-vector extractor for speaker verification (SV) and we optimize its architecture for fast and effective discriminative training. We were motivated by computational and memory requirements caused by the large number of parameters of the original generative i-vector model. Our aim is to preserve the power of the original generative model, and at the same time focus the model towards extraction of speaker-related information. We show that it is possible to represent a standard generative i-vector extractor by a model with significantly less parameters and obtain similar performance on SV tasks. We can further refine this compact model by discriminative training and obtain i-vectors that lead to better performance on various SV benchmarks representing different acoustic domains.Comment: Submitted to Interspeech 2019, Graz, Austria. arXiv admin note: substantial text overlap with arXiv:1810.1318

    Investigation of Frame Alignments for GMM-based Digit-prompted Speaker Verification

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    Frame alignments can be computed by different methods in GMM-based speaker verification. By incorporating a phonetic Gaussian mixture model (PGMM), we are able to compare the performance using alignments extracted from the deep neural networks (DNN) and the conventional hidden Markov model (HMM) in digit-prompted speaker verification. Based on the different characteristics of these two alignments, we present a novel content verification method to improve the system security without much computational overhead. Our experiments on the RSR2015 Part-3 digit-prompted task show that, the DNN based alignment performs on par with the HMM alignment. The results also demonstrate the effectiveness of the proposed Kullback-Leibler (KL) divergence based scoring to reject speech with incorrect pass-phrases.Comment: accepted by APSIPA ASC 201

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

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    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
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