基于HMM-FNN模型的复杂动态手势识别

复杂动态手势识别是利用视频手势进行人机交互的关键问题.提出一种HMM-FNN模型结构.它整合了隐马尔可夫模型对时序数据的建模能力与模糊神经网络的模糊规则构建与推理能力,并将其应用到复杂动态手势的识别中.复杂动态手势具备两大特点:运动特征的可分解性与定义描述的模糊性.针对这两种特性,复杂手势被分解为手形变化、2D平面运动与Z轴方向运动3个子部分,分别利用HMM进行建模,HMM模型对观察子序列的似然概率被作为FNN的模糊隶属度,通过模糊规则推理,最终得到手势的分类类别.HMM-FNN方法将高维手势特征分解为低维子特征序列,降低了模型的复杂度.此外,它还可以充分利用人的经验辅助模型结构的创建与优化.实验表明,该方法是一种有效的复杂动态手势识别方法,并且优于传统的HMM模型方法.

多权值神经元网络仿生模式识别方法在低训练样本数量非特定人语音识别中与HMM及DTW的比较研究

本文将基于多权值神经元网络的仿生模式识别方法用于连续语音有限词汇量固定词组识别的研究中,并将其识别效果与HMM方法及DTW方法进行了比较分析.以15个词组的词汇表做测试,通过调整这三种识别算法的参数,在它们的拒识率相同的情况下,针对参加训练的词汇,比较他们的错误识别率(某类误认为他类);针对未参加训练的词汇,比较他们的错误接受率(误认为某类).结果表明,在低训练样本数量的情况下,仿生模式识别方法能获得更好的识别效果.

Study on Closed-set Speaker Identification Based on Biomimetic Pattern Recognition

In this paper, a new classifier of speaker identification has been proposed, which is based on Biomimetic pattern recognition (BPR). Distinguished from traditional speaker recognition methods, such as DWT, HMM, GMM, SVM and so on, the proposed classifier is constructed by some finite sub-space which is reasonable covering of the points in high dimensional space according to distributing characteristic of speech feature points. It has been used in the system of speaker identification. Experiment results show that better effect could be obtained especially with lesser samples. Furthermore, the proposed classifier employs a much simpler modeling structure as compared to the GMM. In addition, the basic idea "cognition" of Biomimetic pattern recognition (BPR) results in no requirement of retraining the old system for enrolling new speakers.

Digits speech recognition based on geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems.We observe that this may be true for a recognition tasks based on geometrical learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions via the Hilbert transform. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy, Experiments show method based on ICA and geometrical learning outperforms HMM in different number of train samples.

Information geometry theory of high-dimension space and application for speaker independent continuous digit speech recognition

In the light of descriptive geometry and notions in set theory, this paper re-defines the basic elements in space such as curve and surface and so on, presents some fundamental notions with respect to the point cover based on the High-dimension space (HDS) point covering theory, finally takes points from mapping part of speech signals to HDS, so as to analyze distribution information of these speech points in HDS, and various geometric covering objects for speech points and their relationship. Besides, this paper also proposes a new algorithm for speaker independent continuous digit speech recognition based on the HDS point dynamic searching theory without end-points detection and segmentation. First from the different digit syllables in real continuous digit speech, we establish the covering area in feature space for continuous speech. During recognition, we make use of the point covering dynamic searching theory in HDS to do recognition, and then get the satisfying recognized results. At last, compared to HMM (Hidden Markov models)-based method, from the development trend of the comparing results, as sample amount increasing, the difference of recognition rate between two methods will decrease slowly, while sample amount approaching to be very large, two recognition rates all close to 100% little by little. As seen from the results, the recognition rate of HDS point covering method is higher than that of in HMM (Hidden Markov models) based method, because, the point covering describes the morphological distribution for speech in HDS, whereas HMM-based method is only a probability distribution, whose accuracy is certainly inferior to point covering.

Keyword spotting system based on biomimetic pattern recognition

The mandarin keyword spotting system was investigated, and a new approach was proposed based on the principle of homology continuity and point location analysis in high-dimensional space geometry theory which are both parts of biomimetic pattern recognition theory. This approach constructed a hyper-polyhedron with sample points in the training set and calculated the distance between each test point and the hyper-polyhedron. The classification resulted from the value of those distances. The approach was tested by a speech database which was created by ourselves. The performance was compared with the classic HMM approach and the results show that the new approach is much better than HMM approach when the training data is not sufficient.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

Continuous speech research based on HyperSausage Neuron

In this paper, we presents HyperSausage Neuron based on the High-Dimension Space(HDS), and proposes a new algorithm for speaker independent continuous digit speech recognition. At last, compared to HMM-based method, the recognition rate of HyperSausage Neuron method is higher than that of in HMM-based method.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

Continuous speech research based on HyperSausage Neuron

In this paper, we presents HyperSausage Neuron based on the High-Dimension Space(HDS), and proposes a new algorithm for speaker independent continuous digit speech recognition. At last, compared to HMM-based method, the recognition rate of HyperSausage Neuron method is higher than that of in HMM-based method.

半导体神经计算机的硬件实现及其在连续语音识别中的应用

本文首先分析了以PC机作为宿主机的半导体神经网络处理机CASSANDRA-Ⅰ，进一步介绍了新的半导体神经计算机CASSANDRA-Ⅱ的系统实现和功能特性，并将其应用到问候语语音识别中，实验结果表明CASSANDRA-Ⅱ神经计算机识别结果优于HMM模型的识别结果。

基于符号时间序列分析技术的电机故障诊断方法研究

随着经济建设的快速发展和电气化程度的不断提高，电机已被广泛应用于工业、农业、国防及人们日常生活的各个领域。从全球范围看，电机的用电量平均占世界用电总量的50％以上、占工业用电量的70％左右，然而在电机消耗的电能中有相当一部分被浪费掉了，其中电机带故障运行是造成电机运行效率偏低，能源浪费严重的主要原因之一。 电机在线监测及故障诊断系统对于减少由于电机故障引发的人员、财产的损失，减少由于故障引发的异常状态而导致的能源浪费有着重要的现实意义。在电机故障危害产生前发现故障并进行维护是电机故障诊断的核心思想，在保证电机故障诊断系统准确性的同时，系统的快速性与鲁棒性显得尤为重要。基于此，本论文从寻求系统的快速、稳定的性能入手，提出了基于符号时间序列分析的感应电机故障诊断框架，重点研究了计算代价小、噪声干扰不敏感的诊断方法，以期提高感应电机故障诊断系统的快速性与鲁棒性。论文的主要工作有： 1． 论文首先构建了一个基于符号时间序列分析的电机故障诊断框架，将电 机故障诊断分解为信号预处理、符号区间划分、符号统计分析三部分，有机地融合了统计分析、信号处理、信息论、模式识别等理论和方法，利用符号时间序列分析技术在强噪声中准确识别系统状态模式的良好性能，可以有效地解决电机故障诊断问题，并实现电机故障诊断量化分析，是对探索电机在线监测与诊断新方法的一次有益的尝试。 2．引入提升小波对信号进行前期处理，并针对常规提升小波固定预测滤波器的局限性，提出了基于梯度信息的自适应提升小波预测方法。该方法中预测滤波器并不是固定的，而是利用梯度的信息来确定预测算子。根据信号的陡峭程度选择预测算子可以更准确地预测信号，从而使原始信号中的平滑特征和陡峭特征可以在小波变换中完好地保留下来。仿真实验及实验室实验结果表明该方法可以有效地保留信号中蕴含的重要的特征信息，对于以提取、识别信号中特征信息为主的故障诊断技术来说具有非常重要的意义。 3．针对所采集现场信号的非均匀分布特点，论文提出了一种自适应符号化划分方法，既可以确保符号在数据密集区间和数据稀疏区间的合理分配，提高符号的利用率，又可以灵活地适应信号的特征，增强诊断系统对微弱故障信号的敏感度。故障诊断实验表明该方法简单有效，实现了故障初期的快速诊断，并且较平均区间划分方法有着更高的计算效率、更明显的诊断效果。 4．将相对熵的概念引入基于符号时间序列分析的电机故障诊断框架中，针对电机故障严重程度量化分析问题，提出了基于模糊相对熵及加权模糊相对熵的符号统计分析方法，并将该方法应用于感应电机的故障诊断与识别，建立了电机故障诊断模型。该方法可以更合理、充分地利用信息丰富的符号区间所蕴含的故障信息，实现了电机故障诊断与故障严重程度的识别。实验结果验证了该方法的合理性、有效性和可靠性。 5．将隐马尔可夫模型（HMM）引入到基于符号时间序列分析的电机故障诊断框架中，构造了基于HMM的电机故障诊断模型，并对HMM阶数选取问题给出了一个基于符号出现不确定信息熵的HMM阶数选取原则，使得模型在满足精度要求的同时，又尽可能地减少模型的计算代价，有效地提高了故障诊断的效率及可靠性。实验结果表明基于HMM的电机故障诊断方法有效地实现了电机转子断条故障、匝间短路故障的诊断与量化分析。