As the use of biometrics becomes more wide-spread, the privacy concerns that stem from the use of biometrics are becoming more apparent. As the usage of mobile devices grows, so does the desire to implement biometric identification into such devices. A large majority of mobile devices being used are mobile phones. While work is being done to implement different types of biometrics into mobile phones, such as photo based biometrics, voice is a more natural choice. The idea of voice as a biometric identifier has been around a long time. One of the major concerns with using voice as an identifier is the instability of voice. We have developed a protocol that addresses those instabilities and preserves privacy. This paper describes a novel protocol that allows a user to authenticate using voice on a mobile remote device without compromising their privacy. We first discuss the Vaulted Verification protocol, which has recently been introduced in research literature, and then describe its limitations. We then introduce a novel adaptation and extension of the Vaulted Verification protocol to voice, dubbed Vaulted Voice Verification ( V 3 ). Following that we show a performance evaluation and then conclude with a discussion of security and future work.
In the last couple of years, different groups have begun to focus on using template protection with speech using GMMs. Teoh and Chong @cite_13 used Probabilistic Random Projections (PRP) to protect their speaker template while doing speaker verification. In their research, the template is hidden in a process of random projections in a subspace. The EER obtained in their experiment range between 0 differs from this work because it is a client-server protocol that can be used on mobile devices, and the template is not stored on the server. also improves over the technique discussed by Teoh and Chong in that we use multiple templates instead of a single template, this improves the certainty in the final score.
@cite_13
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Due to biometric template characteristics that are susceptible to non-revocable and privacy invasion, cancellable biometrics has been introduced to tackle these issues. In this paper, we present a two-factor cancellable formulation for speech biometrics, which we refer as probabilistic random projection (PRP). PRP offers strong protection on speech template by hiding the actual speech feature through the random subspace projection process. Besides, the speech template is replaceable and can be reissued when it is compromised. Our proposed method enables the generation of different speech templates from the same speech feature, which means linkability is not exited between the speech templates. The formulation of the cancellable biometrics retains its performance as for the conventional biometric. Besides that, we also propose 2D subspace projection techniques for speech feature extraction, namely 2D Principle Component Analysis (2DPCA) and 2D CLAss-Featuring Information Compression (2DCLAFIC) to accommodate the requirements of PRP formulation. This paper considers acoustic beamforming for noise robust automatic speech recognition. A beamformer attenuates background noise by enhancing sound components coming from a direction specified by a steering vector. Hence, accurate steering vector estimation is paramount for successful noise reduction. Recently, time-frequency masking has been proposed to estimate the steering vectors that are used for a beamformer. In particular, we have developed a new form of this approach, which uses a speech spectral model based on a complex Gaussian mixture model CGMM to estimate the time-frequency masks needed for steering vector estimation, and extended the CGMM-based beamformer to an online speech enhancement scenario. Our previous experiments showed that the proposed CGMM-based approach outperforms a recently proposed mask estimator based on a Watson mixture model and the baseline speech enhancement system of the CHiME-3 challenge. This paper provides additional experimental results for our online processing, which achieves performance comparable to that of batch processing with a suitable block-batch size. This online version reduces the CHiME-3 word error rate WER on the evaluation set from 8.37 to 8.06 . Moreover, in this paper, we introduce a probabilistic prior distribution for a spatial correlation matrix a CGMM parameter, which enables more stable steering vector estimation in the presence of interfering speakers. In practice, the performance of the proposed online beamformer degrades with observations that contain only noise or and interference because of the failure of the CGMM parameter estimation. The introduced spatial prior enables the target speaker's parameter to avoid overfitting to noise or and interference. Experimental results show that the spatial prior reduces the WER from 38.4 to 29.2 in a conversation recognition task compared with the CGMM-based approach without the prior, and outperforms a conventional online speech enhancement approach. Probabilistic graphical models, such as partially observable Markov decision processes (POMDPs), have been used in stochastic spoken dialog systems to handle the inherent uncertainty in speech recognition and language understanding. Such dialog systems suffer from the fact that only a relatively small number of domain variables are allowed in the model, so as to ensure the generation of good-quality dialog policies. At the same time, the non-language perception modalities on robots, such as vision-based facial expression recognition and Lidar-based distance detection, can hardly be integrated into this process. In this paper, we use a probabilistic commonsense reasoner to “guide” our POMDP-based dialog manager, and present a principled, multimodal dialog management (MDM) framework that allows the robot's dialog belief state to be seamlessly updated by both observations of human spoken language, and exogenous events such as the change of human facial expressions. The MDM approach has been implemented and evaluated both in simulation and on a real mobile robot using guidance tasks. This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability space because it models both speaker and channel variabilities. Two speaker verification systems are proposed which use this new representation. The first system is a support vector machine-based system that uses the cosine kernel to estimate the similarity between the input data. The second system directly uses the cosine similarity as the final decision score. We tested three channel compensation techniques in the total variability space, which are within-class covariance normalization (WCCN), linear discriminate analysis (LDA), and nuisance attribute projection (NAP). We found that the best results are obtained when LDA is followed by WCCN. We achieved an equal error rate (EER) of 1.12 and MinDCF of 0.0094 using the cosine distance scoring on the male English trials of the core condition of the NIST 2008 Speaker Recognition Evaluation dataset. We also obtained 4 absolute EER improvement for both-gender trials on the 10 s-10 s condition compared to the classical joint factor analysis scoring.