Adversarial Speaker Adaptation

We propose a novel adversarial speaker adaptation (ASA) scheme, in which adversarial learning is applied to regularize the distribution of deep hidden features in a speaker-dependent (SD) deep neural network (DNN) acoustic model to be close to that of a fixed speaker-independent (SI) DNN acoustic model during adaptation. An additional discriminator network is introduced to distinguish the deep features generated by the SD model from those produced by the SI model. In ASA, with a fixed SI model as the reference, an SD model is jointly optimized with the discriminator network to minimize the senone classification loss, and simultaneously to mini-maximize the SI/SD discrimination loss on the adaptation data. With ASA, a senone-discriminative deep feature is learned in the SD model with a similar distribution to that of the SI model. With such a regularized and adapted deep feature, the SD model can perform improved automatic speech recognition on the target speaker's speech. Evaluated on the Microsoft short message dictation dataset, ASA achieves 14.4% and 7.9% relative word error rate improvements for supervised and unsupervised adaptation, respectively, over an SI model trained from 2600 hours data, with 200 adaptation utterances per speaker.Comment: 5 pages, 2 figures, ICASSP 201

Attentive Adversarial Learning for Domain-Invariant Training

Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network (DNN) acoustic model and is trained to improve their domain-invariance by optimizing an adversarial loss function. In this work, we propose an attentive ADIT (AADIT) in which we advance the domain classifier with an attention mechanism to automatically weight the input deep features according to their importance in domain classification. With this attentive re-weighting, AADIT can focus on the domain normalization of phonetic components that are more susceptible to domain variability and generates deep features with improved domain-invariance and senone-discriminativity over ADIT. Most importantly, the attention block serves only as an external component to the DNN acoustic model and is not involved in ASR, so AADIT can be used to improve the acoustic modeling with any DNN architectures. More generally, the same methodology can improve any adversarial learning system with an auxiliary discriminator. Evaluated on CHiME-3 dataset, the AADIT achieves 13.6% and 9.3% relative WER improvements, respectively, over a multi-conditional model and a strong ADIT baseline.Comment: 5 pages, 1 figure, ICASSP 201

Regional Price Differences in Urban China 1986-2001: Estimation and Implication

Despite the intensive efforts made by economists to examine regional income inequality in China, limited attention has been paid to disentangle the contribution of regional price differentials. This paper examines regional price differential in urban China over the period 1986 to 2001. Spatial Price Index (SPI) is normally calculated using the Basket Cost Method, which defines a national basket and measures price variation of this common basket across different regions. The weakness of this method is that it arbitrarily assumes consumers’ preferences and has a strong reliance on good regional level price data, which are often not available. This paper adopts the Engel’s curve approach to estimate a Spatial Price Index for different provinces. The SPI obtained from the Engel’s curve approach indicates larger regional price variations than those obtained from the Basket Cost method. Further, regional price variations in urban China increased significantly during the late 1980s to early 1990s, stabilized at a relatively high level during the mid to end 1990s. Adjusting for the regional price variations our finding suggests that regional income inequality increased the most between the late 1980s and early 1990s, and stabilized in the mid 1990s, which contradicts previous findings using unadjusted income.spatial price index, Engel’s curve, income inequality, China

Lumber-Based Mass Timber Products in Construction

This chapter provides information related to commonly used wood construction methods (i.e., light-frame, post-and-beam, and mass timber) and mass timber products. It briefly discusses the manufacturing of four major lumber-based mass timber products (i.e., glue-laminated timber, nail-laminated timber, dowel-laminated timber, and cross-laminated timber), and their available dimensions and typical applications. The discussion also addresses primary lumber products, such as dimension lumber, machine stress-rated lumber, and finger-joined lumber, which are the building blocks from which mass timber products are manufactured. Advantages of using wood in construction are illustrated by examples largely from North American practices. The life cycle assessment concept is also introduced

Conditional Teacher-Student Learning

The teacher-student (T/S) learning has been shown to be effective for a variety of problems such as domain adaptation and model compression. One shortcoming of the T/S learning is that a teacher model, not always perfect, sporadically produces wrong guidance in form of posterior probabilities that misleads the student model towards a suboptimal performance. To overcome this problem, we propose a conditional T/S learning scheme, in which a "smart" student model selectively chooses to learn from either the teacher model or the ground truth labels conditioned on whether the teacher can correctly predict the ground truth. Unlike a naive linear combination of the two knowledge sources, the conditional learning is exclusively engaged with the teacher model when the teacher model's prediction is correct, and otherwise backs off to the ground truth. Thus, the student model is able to learn effectively from the teacher and even potentially surpass the teacher. We examine the proposed learning scheme on two tasks: domain adaptation on CHiME-3 dataset and speaker adaptation on Microsoft short message dictation dataset. The proposed method achieves 9.8% and 12.8% relative word error rate reductions, respectively, over T/S learning for environment adaptation and speaker-independent model for speaker adaptation.Comment: 5 pages, 1 figure, ICASSP 201

Role of thermal noise in tripartite quantum steering

The influence of thermal noise on bipartite and tripartite quantum steering induced by a short laser pulse in a hybrid three-mode optomechanical system is investigated. The calculation is carried out under the bad cavity limit, the adiabatic approximation of a slowly varying amplitude of the cavity mode, and with the assumption of driving the cavity mode with a blue detuned strong laser pulse. Under such conditions, explicit expressions of the bipartite and tripartite steering parameters are obtained, and the concept of collective tripartite quantum steering, recently introduced by He and Reid [Phys. Rev. Lett. 111, 250403 (2013)], is clearly explored. It is found that both bipartite and tripartite steering parameters are sensitive functions of the initial state of the modes and distinctly different steering behaviour could be observed depending on whether the modes were initially in a thermal state or not. We find that the initial thermal noise is more effective in destroying the bipartite rather than the tripartite steering which, on the other hand, can persist even for a large thermal noise. For the initial vacuum state of a steered mode, the tripartite steering exists over the entire interaction time even if the steering modes are in very noisy thermal states. When the steered mode is initially in a thermal state, it can be collectively steered by the other modes. There are thresholds for the average number of the thermal photons above which the existing tripartite steering appears as the collective steering. Finally, we point out that the collective steering may provide a resource in a hybrid quantum network for quantum secret sharing protocol.Comment: 13 pages, 9 figure

Lattice Gluon Propagator in the Landau Gauge: A Study Using Anisotropic Lattices

Lattice gluon propagators are studied using tadpole and Symanzik improved gauge action in Landau gauge. The study is performed using anisotropic lattices with asymmetric volumes. The Landau gauge dressing function for the gluon propagator measured on the lattice is fitted according to a leading power behavior: $Z(q^2)\simeq (q^2)^{2\kappa}$ with an exponent $\kappa$ at small momenta. The gluon propagators are also fitted using other models and the results are compared. Our result is compatible with a finite gluon propagator at zero momentum in Landau gauge.Comment: 14 pages, 4 figure