A cyber-physical system (CPS) is known as a mix system composed of computational and physical capabilities. The fast development of CPS brings new security and privacy requirements. Code reuse attacks that affect the correct behavior of software by exploiting memory corruption vulnerabilities and reusing existing code may also be threats to CPS. Various defense techniques are proposed in recent years as countermeasures to emerging code reuse attacks. However, they may fail to fulfill the security requirement well because they cannot protect the indirect function calls properly when it comes to dynamic code reuse attacks aiming at forward edges of control-flow graph (CFG). In this paper, we propose P-CFI, a fine-grained control-flow integrity (CFI) method, to protect CPS against memory-related attacks. We use points-to analysis to construct the legitimate target set for every indirect call cite and check whether the target of the indirect call cite is in the legitimate target set at runtime. We implement a prototype of P-CFI on LLVM and evaluate both its functionality and performance. Security analysis proves that P-CFI can mitigate the dynamic code reuse attack based on forward edges of CFG. Performance evaluation shows that P-CFI can protect CPS from dynamic code reuse attacks with trivial time overhead between 0.1% and 3.5% (Copyright © 2018 John Wiley & Sons, Ltd.).

We present a novel language identification technique using our recently developed deep-structured conditional random fields (CRFs). The deep-structured CRF is a multi-layer CRF model in which each higher layer's input observation sequence consists of the lower layer's observation sequence and the resulting lower layer's frame-level marginal probabilities. In this paper we extend the original deep-structured CRF by allowing for distinct state representations at different layers and demonstrate its benefits. We propose an unsupervised algorithm to pre-train the intermediate layers by casting it as a multi-objective programming problem that is aimed at minimizing the average frame-level conditional entropy while maximizing the state occupation entropy. Empirical evaluation on a seven-language/dialect voice mail routing task showed that our approach can achieve a routing accuracy (RA) of 86.4% and average equal error rate (EER) of 6.6%. These results are significantly better than the 82.5% RA and 7.5% average EER obtained using the Gaussian mixture model trained with the maximum mutual information criterion but slightly worse than the 87.7% RA and 6.4% EER achieved using the support vector machine with model pushing on the Gaussian super vector (GSV).

It is widely known that the quality of confidence measure is critical for speech applications. In this paper, we present our recent work on improving word confidence scores by calibrating them using a small set of calibration data when only the recognized word sequence and associated raw confidence scores are made available. The core of our technique is the maximum entropy model with distribution constraints which naturally and effectively make use of the word distribution, the raw confidence-score distribution, and the context information. We demonstrate the effectiveness of our approach by showing that it can achieve relative 38% mean square error (MSE), 39% negative normalized likelihood (NNLL), and 23% equal error rate (EER) reduction on a voice mail transcription data set and relative 35% MSE, 45% NNLL, and 35% EER reduction on a command and control data set.

In this paper, we propose a Bark-scale shift based piecewise nonlinear warping function for speaker normalization, and a joint frequency discontinuity and energy attenuation detection algorithm to estimate the second subglottal resonance (Sg2). We then apply Sg2 for rapid speaker normalization. Experimental results on children’s speech recognition show that the proposed nonlinear warping function is more effective for speaker normalization than linear frequency warping. Compared to maximum likelihood based grid search methods, Sg2 normalization is more efficient and achieves comparable or better performance, especially for limited normalization data.

Speaker normalization typically focuses on variabilities of the supra-glottal (vocal tract) resonances, which constitute a major cause of spectral mismatch. Recent studies show that the subglottal airways also affect spectral properties of speech sounds. This paper presents a speaker normalization method based on estimating the second and third subglottal resonances. Since the subglottal airways do not change for a specific speaker, the subglottal resonances are independent of the sound type (i.e., vowel, consonant, etc.) and remain constant for a given speaker. This context-free property makes the proposed method suitable for limited data speaker adaptation. This method is computationally more efficient than maximum-likelihood based VTLN, with performance better than VTLN especially for limited adaptation data. Experimental results confirm that this method performs well in a variety of testing conditions and tasks.

In previous work [1], we proposed a speaker adaptation technique based on the second subglottal resonance (Sg2), which showed good performance relative to vocal tract length normalization (VTLN). In this paper, we propose a more reliable algorithm for automatically estimating Sg2 from speech signals. The algorithm is calibrated on children’s speech data collected simultaneously with accelerometer recordings from which Sg2 frequencies can be directly measured. To investigate whether Sg2 frequencies are independent of speech content and language, we perform a cross-language study with bilingual Spanish-English children. The study verifies that Sg2 is approximately constant for a given speaker and thus can be a good candidate for limited data speaker normalization and cross-language adaptation. We then present a cross-language speaker normalization method based on Sg2, which is computationally more efficient than maximum-likelihood based VTLN, and performs more robustly than VTLN.

This paper describes the design and realization of an automatic system for assessing and evaluating the language and literacy skills of young children. This system was developed in the context of the TBALL (technology based assessment of language and literacy) project and aims at automatically assessing the English literacy skills of both native talkers of American English and Mexican-American children in grades K-2. The automatic assessments were carried out employing appropriate speech recognition and understanding techniques. In this paper, we describe the system focusing on the role of the multiple sources of information at our disposal. We present the content of the assessment system, discuss some issues in creating a child-friendly interface, and how to provide a suitable feedback to the teachers. In addition, we will discuss the different assessment modules and the different algorithms used for speech analysis.

Spectral mismatch between training and testing utterances can cause significant degradation in the performance of automatic speech recognition (ASR) systems. Speaker adaptation and speaker normalization techniques are usually applied to address this issue. One way to reduce spectral mismatch is to reshape the spectrum by aligning corresponding formant peaks. There are various levels of mismatch in formant structures. In this paper, regression-tree-based phoneme- and state-level spectral peak alignment is proposed for rapid speaker adaptation using linearization of the vocal tract length normalization (VTLN) technique. This method is investigated in a maximum-likelihood linear regression (MLLR)-like framework, taking advantage of both the efficiency of frequency warping (VTLN) and the reliability of statistical estimations (MLLR). Two different regression classes are investigated: one based on phonetic classes (using combined knowledge and data-driven techniques) and the other based on Gaussian mixture classes. Compared to MLLR, VTLN, and global peak alignment, improved performance can be obtained for both supervised and unsupervised adaptations for both medium vocabulary (the RM1 database) and connected digits recognition (the TIDIGITS database) tasks. Performance improvements are largest with limited adaptation data which is often the case for ASR applications, and these improvements are shown to be statistically significant.

In this paper, regression-tree based spectral peak alignment is proposed for rapid speaker adaptation using the linearization of VTLN. Two different regression classes are investigated: phonetic classes (using combined knowledge and data-driven techniques) and mixture classes. Compared to MLLR and VTLN, improved performance can be obtained for both supervised and unsuper-vised adaptations on both medium vocabulary and connected dig-its recognition tasks. To further improve the performance, MLLR was integrated into this regression-tree based peak alignment. Ex-perimental results show that the performance improvements can be achieved even with limited adaptation data. Index Terms: speaker adaptation, peak alignment, regression tree 1.