Homogenous Ensemble Phonotactic Language Recognition Based on SVM Supervector Reconstruction

Currently, acoustic spoken language recognition (SLR) and phonotactic SLR systems are widely used language recognition systems. To achieve better performance, researchers combine multiple subsystems with the results often much better than a single SLR system. Phonotactic SLR subsystems may vary in the acoustic features vectors or include multiple language-specific phone recognizers and different acoustic models. These methods achieve good performance but usually compute at high computational cost. In this paper, a new diversification for phonotactic language recognition systems is proposed using vector space models by support vector machine (SVM) supervector reconstruction (SSR). In this architecture, the subsystems share the same feature extraction, decoding, and N-gram counting preprocessing steps, but model in a different vector space by using the SSR algorithm without significant additional computation. We term this a homogeneous ensemble phonotactic language recognition (HEPLR) system. The system integrates three different SVM supervector reconstruction algorithms, including relative SVM supervector reconstruction, functional SVM supervector reconstruction, and perturbing SVM supervector reconstruction. All of the algorithms are incorporated using a linear discriminant analysis-maximum mutual information (LDA-MMI) backend for improving language recognition evaluation (LRE) accuracy. Evaluated on the National Institute of Standards and Technology (NIST) LRE 2009 task, the proposed HEPLR system achieves better performance than a baseline phone recognition-vector space modeling (PR-VSM) system with minimal extra computational cost. The performance of the HEPLR system yields 1.39%, 3.63%, and 14.79% equal error rate (EER), representing 6.06%, 10.15%, and 10.53% relative improvements over the baseline system, respectively, for the 30-, 10-, and 3-s test conditions

RNN Language Model with Word Clustering and Class-based Output Layer

The recurrent neural network language model (RNNLM) has shown significant promise for statistical language modeling. In this work, a new class-based output layer method is introduced to further improve the RNNLM. In this method, word class information is incorporated into the output layer by utilizing the Brown clustering algorithm to estimate a class-based language model. Experimental results show that the new output layer with word clustering not only improves the convergence obviously but also reduces the perplexity and word error rate in large vocabulary continuous speech recognition

Time–Frequency Cepstral Features and Heteroscedastic Linear Discriminant Analysis for Language Recognition

The shifted delta cepstrum (SDC) is a widely used feature extraction for language recognition (LRE). With a high context width due to incorporation of multiple frames, SDC outperforms traditional delta and acceleration feature vectors. However, it also introduces correlation into the concatenated feature vector, which increases redundancy and may degrade the performance of backend classifiers. In this paper, we first propose a time-frequency cepstral (TFC) feature vector, which is obtained by performing a temporal discrete cosine transform (DCT) on the cepstrum matrix and selecting the transformed elements in a zigzag scan order. Beyond this, we increase discriminability through a heteroscedastic linear discriminant analysis (HLDA) on the full cepstrum matrix. By utilizing block diagonal matrix constraints, the large HLDA problem is then reduced to several smaller HLDA problems, creating a block diagonal HLDA (BDHLDA) algorithm which has much lower computational complexity. The BDHLDA method is finally extended to the GMM domain, using the simpler TFC features during re-estimation to provide significantly improved computation speed. Experiments on NIST 2003 and 2007 LRE evaluation corpora show that TFC is more effective than SDC, and that the GMM-based BDHLDA results in lower equal error rate (EER) and minimum average cost (Cavg) than either TFC or SDC approaches

Exploiting Contextual Information for Prosodic Event Detection Using Auto-Context

Prosody and prosodic boundaries carry significant information regarding linguistics and paralinguistics and are important aspects of speech. In the field of prosodic event detection, many local acoustic features have been investigated; however, contextual information has not yet been thoroughly exploited. The most difficult aspect of this lies in learning the long-distance contextual dependencies effectively and efficiently. To address this problem, we introduce the use of an algorithm called auto-context. In this algorithm, a classifier is first trained based on a set of local acoustic features, after which the generated probabilities are used along with the local features as contextual information to train new classifiers. By iteratively using updated probabilities as the contextual information, the algorithm can accurately model contextual dependencies and improve classification ability. The advantages of this method include its flexible structure and the ability of capturing contextual relationships. When using the auto-context algorithm based on support vector machine, we can improve the detection accuracy by about 3% and F-score by more than 7% on both two-way and four-way pitch accent detections in combination with the acoustic context. For boundary detection, the accuracy improvement is about 1% and the F-score improvement reaches 12%. The new algorithm outperforms conditional random fields, especially on boundary detection in terms of F-score. It also outperforms an n-gram language model on the task of pitch accent detection

Quenched Fe Moment in the Collapsed Tetragonal Phase of Ca1−x_{1-x}Prx_{x}Fe2_2As2_2

We report $^{75}$As NMR studies on single crystals of rare-earth doped iron pnictides superconductor Ca$_{1-x}$Pr$_{x}$Fe$_{2}$As$_{2}$ ($x$=0.075 and 0.15). The $^{75}$As spectra show a chemical pressure effect with doping and a first order structure transition to the collapsed tetragonal phase upon cooling. A sharp drop of the Knight shift is seen below the structural transition, whereas $1/T_1$ is strongly enhanced at low-temperatures. These evidences indicate quenching of Fe local magnetism and short-range ordering of Pr$^{3+}$ moment in the collapsed tetragonal phase. The quenched Fe moment through structure collapse suggests a strong interplay of structure and magnetism, which is important for understanding the nature of the collapsed tetragonal phase.Comment: 5 pages, 5 figure

Entanglement and quantum phase transition in alternating XY spin chain with next-nearest neighbour interactions

By using the method of density-matrix renormalization-group to solve the different spin-spin correlation functions, the nearest-neighbouring entanglement(NNE) and next-nearest-neighbouring entanglement(NNNE) of one-dimensional alternating Heisenberg XY spin chain is investigated in the presence of alternating nearest neighbour interactions of exchange couplings, external magnetic fields and next-nearest neighbouring interactions. For dimerized ferromagnetic spin chain, NNNE appears only above the critical dimerized interaction, meanwhile, the dimerized interaction effects quantum phase transition point and improves NNNE to a large value. We also study the effect of ferromagnetic or antiferromagnetic next-nearest neighboring (NNN) interactions on the dynamics of NNE and NNNE. The ferromagnetic NNN interaction increases and shrinks NNE below and above critical frustrated interaction respectively, while the antiferromagnetic NNN interaction always decreases NNE. The antiferromagnetic NNN interaction results to a larger value of NNNE in comparison to the case when the NNN interaction is ferromagnetic.Comment: 13 pages, 4 figures,. accepted by Chinese Physics B 2008 11 (in press

Holographic non-relativistic fermionic fixed point by the charged dilatonic black hole

Driven by the landscape of garden-variety condensed matter systems, we have investigated how the dual spectral function behaves at the non-relativistic as well as relativistic fermionic fixed point by considering the probe Dirac fermion in an extremal charged dilatonic black hole with zero entropy. Although the pattern for both of the appearance of flat band and emergence of Fermi surface is qualitatively similar to that given by the probe fermion in the extremal Reissner-Nordstrom AdS black hole, we find a distinctly different low energy behavior around the Fermi surface, which can be traced back to the different near horizon geometry. In particular, with the peculiar near horizon geometry of our extremal charged dilatonic black hole, the low energy behavior exhibits the universal linear dispersion relation and scaling property, where the former indicates that the dual liquid is a Fermi one while the latter implies that the dual liquid is not exactly of Landau Fermi type