Hierarchical deep belief networks based point process model for keywords spotting in continuous speech

Point process model keyword spotting (KWS) system has attracted considerable attentions in the areas of keyword spotting by its capacity that can generalize from a relatively small numbers of training examples. But unfortunately, the accuracy level of the point process model is not comparable with the state‐of‐the‐art KWS systems because of the poor modeling capacity of the phoneme detector, which are based on Gaussian Mixture Models. In this paper, focus on improving the performance of detector in point process model, we propose an enhanced version of point process model, which is based on hierarchical deep belief networks (DBNs). Hierarchical DBNs are used as the phoneme detector in this system, and they combine the advantages of both the DBN and the hierarchical architecture for capturing complex statistical patterns in speech while overcoming the inherent flaws of conventional hidden Markov models and multilayer layer perceptron. Experiments results on TIMIT database show that the proposed method can yield 2% improvement. Furthermore, in the case when training examples are extremely limited, it can achieve better results over state‐of‐the‐art KWS systems. Copyright © 2013 John Wiley & Sons, Ltd.     

Simultaneous spotting of signs and fingerspellings based on hierarchical conditional random fields and boostmap embeddings

A sign language consists of two types of action; signs and fingerspellings. Signs are dynamic gestures discriminated by continuous hand motions and hand configurations, while fingerspellings are a combination of continuous hand configurations. Sign language spotting is the task of detection and recognition of signs and fingerspellings in a signed utterance. The internal structures of signs and fingerspellings differ significantly. Therefore, it is difficult to spot signs and fingerspellings simultaneously. In this paper, a novel method for spotting signs and fingerspellings is proposed. It can distinguish signs, fingerspellings and non-sign patterns, and is robust to the various sizes, scales and rotations of the signer's hand. This is achieved through a hierarchical framework consisting of three steps: (1) Candidate segments of signs and fingerspellings are discriminated using a two-layer conditional random field (CRF). (2) Hand shapes of segmented signs and fingerspellings are verified using BoostMap embeddings. (3) The motions of fingerspellings are verified in order to distinguish those which have similar hand shapes and different hand motions. Experiments demonstrate that the proposed method can spot signs and fingerspellings from utterance data at rates of 83% and 78%, respectively.     

The Pleadings Game

The Pleadings Game is a normative formalization and computational model of civil pleading, founded in Roberty Alexy's discourse theory of legal argumentation. The consequences of arguments and counterarguments are modelled using Geffner and Pearl's nonmonotonic logic,conditional entailment. Discourse in focussed using the concepts of issue and relevance. Conflicts between arguments can be resolved by arguing about the validity and priority of rules, at any level. The computational model is fully implemented and has been tested using examples from Article Nine of the Uniform Commercial Code.     

语音关键词识别中基于MLP帧级子词后验概率的置信度方法

针对关键词检测系统中HMM模型框架下置信度计算存在的不足,本文提出了基于MLP帧级子词后验概率的置信度方法。与HMM模型框架下利用声学模型得分与语言模型得分进行置信度计算不同的是,该方法在MLP模型框架下直接将其输出的每帧语音类别的后验概率用于关键词置信度的计算,克服了HMM建模时假设每帧语音的声学特征相互独立以及对状态建模时采用有限混元的高斯分布的不足。关键词检出和置信度确认使用两套不同的模型结构,是两个完全独立的过程,便于融合其他的置信度特征。实验结果表明,本文提出的方法优于HMM框架下主流的置信度计算方法,且与其具有较好的互补性。因此本文将两种不同框架下不同的置信度方法进行融合,系统的等错误率(EER)相对提高了11.5%。     

Augmenting the Discrimination Power of HMM by NN for On-Line Cursive Script Recognition

For on-line handwriting recognition, a hybrid approach that combines the discrimination power of neural networks with the temporal structure of hidden Markov models is presented. Initially, all plausible letter components of an input pattern are detected by using a letter spotting technique based on hidden Markov models. A word hypothesis lattice is generated as a result of the letter spotting. All letter hypotheses in the lattice are evaluated by a neural network character recognizer in order to reinforce letter discrimination power. Then, as a new technique, an island-driven lattice search algorithm is performed to find the optimal path on the word hypothesis lattice which corresponds to the most probable word among the dictionary words. The results of this experiment suggest that the proposed framework works effectively in recognizing English cursive words. In a word recognition test, on average 88.5% word accuracy was obtained.     

基于结构弱点分析的高温构件延寿修复技术

在工程结构中 ,几何突变、冶金不连续以及非均布载荷的作用都会导致结构损伤的局部化 ,能否识别出这些薄弱点对结构的延寿修复具有重要意义。本文采用损伤耦合有限元法对各种高温构件进行分析 ,在蠕变损伤观点的基础上发展高温构件延寿修复的结构弱点分析技术 ,并对典型高温构件的延寿修复提出了指导建议。     

Keyword spotting using an evolutionary-based classifier and discriminative features

Keyword spotting refers to detection of all occurrences of any given keyword in input speech utterances. In this paper, we define a keyword spotter as a binary classifier that separates a class of sentences containing a target keyword from a class of sentences which do not include the target keyword. In order to discriminate the mentioned classes, an efficient classification method and a suitable feature set are to be studied. For the classification method, we propose an evolutionary algorithm to train the separating hyper-plane between the two classes. As our discriminative feature set, we propose two confidence measure functions. The first confidence measure function computes the possibility of phonemes presence in the speech frames, and the second one determines the duration of each phoneme. We define these functions based on the acoustic, spectral and statistical features of speech. The results on TIMIT indicate that the proposed evolutionary-based discriminative keyword spotter has lower computational complexity and higher speed in both test and train phases, in comparison to the SVM-based discriminative keyword spotter. Additionally, the proposed system is robust in noisy conditions.     

模糊模式识别在语音关键词识别中的应用

针对仿生模式识别聚类过程中出现的类间交叠现象,提出了一种模糊模式识别算法,使系统拒识率降低了11个百分点,提高了系统的正确识别率,识别效果大为改善。