一种新的ATM网VBR视频业务模型

许多研究工作表明ＶＢＲ视频业务的每帧信元数服从Ｇａｍｍａ分布，便是考虑这一性质的视频业务模型却很少。本文提出一种新的视频业务模型，一阶自回归Ｇａｍｍａ序列，以值是Ｇａｍｍａ分布的随机变量，文中给出ＧＡＲ模型的定义及有关性质，对ＧＡＲ（１）视频模型的参数估计方法也作了研究。     

正反向隐马尔可夫模型及其在连续语音识别中的应用

本文针对语音信号中客观存在的正、反向依赖特性，明确提出了用条件概率的概念来定量表述语音信号的这种正、反向的马尔可大依赖关系，提出了描述语音信号这种正反向依赖关系的正反向隐马尔可夫模型（ＨＭＭ），并用实验证明了仅仅利用语音反向依赖关系语音识别同样也能获得相当可观的识别性能。接着，本文针对孤立字和连续语音两种不同的识别任务，研究了在语音识别中同时利用这两种依赖信息的方法，并提出了一种连续语音识别中的新的搜索算法──正反向分半混合搜索。这种方法利用基于正向ＨＭＭ的正向Ｖｉｔｅｒｂｉ搜索和基于反向ＨＭＭ的反向Ｖｉｔｅｒｂｉ搜索的中间结果来有效地结合正反向依赖信息，实验证明正反向分半混合搜索方法确实一致地优于单用任何一种依赖信息的单向搜索识别方法。     

发展光纤模块和光连接器提高数字光通信系统品质

目前,ＭＴ—ＲＪ光连接器已被 广泛应用在 １００Ｍｂ／ｓ快速以太网及千兆位（Ｇｂｉｔ）以太网中。ＭＴ—ＲＪ连接器采用一个塑料套管,简化了装配难度,也降低了成本,其较小的端口尺寸也相应降低了千兆比特系统的辐射噪声。 安捷伦（Ａｇｉｌｅｎｔ）科技公司的ＭＴ—ＲＪ小封装光纤模块（ＨＦＢＲ／ＨＦＣＴ－５９１０／５９１２在数码速率高达１．２５Ｇｂ／ｓ的应用中具有宽广的适用领域。本文将重点介绍该系列产品在光纤信道以及千兆以大网的应用。前为ＨＦＢＲ的模块适用于多模光纤,前为ＨＦＣＴ的模块适用于单模光纤;后为５９…     

New Semiconductor Thin-Film Microcavity Surface Emiters

ＮｅｗＳｅｍｉｃｏｎｄｕｃｔｏｒＴｈｉｎＦｉｌｍＭｉｃｒｏｃａｖｉｔｙＳｕｒｆａｃｅＥｍｉｔｅｒｓＲ．Ｘ．Ｊｉｎ１Ｄ．Ｂｏｇｇａｙａｒａｐｕ２Ｍ．Ｓ．Ｔｏｂｉｎ１Ｒ．Ｐ．Ｌｅａｖｉｔ１Ｈ．Ｍ．Ｇｉｂｂｓ３Ｇ．Ｋｈｉｔｒｏｖａ３Ｆ．Ｊａｈｎｋｅ４Ｓ．...     

语音识别隐马尔可夫模型的改进

由于在语音识别中被广泛应用的隐马尔可夫模型是一重马尔可夫模型，它不能充分地描述语音信号的时间相依性。虽然理论上可将ＨＭＭ扩展成多重马尔可夫模型，但由于所需运算量和存储量将成指数增长而使其难以应用。因此，本文提出一种新模型，它是由ＨＭＭ与一个能描述语音信号时间相依性的多维高斯密度函数相结合构成的。本文从理论上论证了新模型的合理性。对汉语不计声调的全部４０９个单音节的识别实验结果表明：新模型的识别率显     

HFBR/HFCT-5910/5912光纤模块的特点及原理

目前,ＭＴ－ＲＪ光连接器已被广泛应用在１００Ｍｂ／ｓ快速以太网及千兆位（Ｇｂｉｔ）以太网中。ＭＴ－ＲＪ连接器采用一个塑料套管,简化了装配难度,也降低了成本,其较小的端口尺寸也相应降低了千兆比特系统的辐射噪声。安捷伦（Ａｇｉｌｅｎｔ）科技公司的ＭＴ－ＲＪ小封装光纤模块（ＨＦＢＲ／ＨＦＣＴ－５９１０／５９１２）在数码速率高达１．２５ＧＢｄ的应用中具有宽广的适用领域。本文将重点介绍该系列产品在光纤信道以及千兆以太网的应用。前缀为ＨＦＢＲ的模块适用于多模光纤;前缀为ＨＦＣＴ的模块适用于单模光纤。后缀为５…     

语音识别HMM中引入帧间相关信息的一种参数化模型

虽然隐马尔可夫模型（ＨＭＭ）是当前最为流行的语音识别模型，但由于一般都采用了状态输出独立假设，因此存在着不能描述语音现象中时间相关性的固有缺陷，本文提出的新模型对语音状态输出特征矢量序列的静态和动态特性信息分别进行参数化建模，然后将它们结合在一起，由此在基于段长分布的ＨＭＭ（ＤＤＢＨＭＭ）中引入了帧间相关信息，这种上引入帧间相关信息的ＨＭＭ能够更为精确地描述真实的语音现象。本文在给出新模型的框架后     

特定人汉语数码语音抗噪识别方法

本文提出一种连续隐邓尔可夫模型（ＣＨＭＭ）和人工神经网络（ＡＮＮ）相结合的鲁棒性识别方法。用于噪声环境下特定人数码语音识别,该方法以ＣＨＭＭ的输出作为系统的识别矢量,利用人工神经网络的模式分类和自学习功能,从识别矢量空间中提取语音预识别矢量,再由识别结果进行识别输出。实验证明,这种基于ＣＨＭＭＡＮＮ的数码语音识别方法明显地提高了系统的噪声鲁棒性,适用于中小词表语音识别系统。     

γ／X^α约束下的动态光散射函数

本文给出了激光散射处在γ／Ｘ＾α约束模型下散射粒子的速度相关函数及位移二阶矩函数,并将所得结果应用到动态光散射技术的理论研究中：将入射光和散射光均视为理想平面光波,将散射作视为γ／Ｘ＾αＸＱＧＫＧＨ Ｒ ＡＥＴ ＴＭＤＦＯＵＧ ＢＢ ＷＹＳＷＧＫ ,ＦＧＨＩＦ     

语音帧间相关信息对基于HMM系统识别精度的影响

本文探讨经短时信号处理后的语音信号帧间相关信息对基于隐马尔可夫模型（ＨＭＭ）的语音识别系统识虽精度的影响，鉴于ＨＭＭ的输出独立假设导致语音帧间相关信息的损失，本文提出了一种描述帧间相关信息的统计模型－马尔可夫链（ＭＣＭ）用来弥补ＨＭＭ在这方面的缺陷；经非特定人和多话者孤立字实验表明，用ＭＣＭ作为ＨＭＭ的辅助模型，可将原有ＨＭＭ系统的识别率提高约１～６个百分点。     

一种引入帧间相关信息的HMM语音识别方法

该文提出了一种基于复数帧段输入HMM的语音识别方法,它采用相继的复数帧组成的特征参数向量作为语音识别HMM的输入,能有效地在语音识别HMM中引入帧间相关信息。为了进一步改善复数帧段输入HMM的输出概率分布函数,作者还提出了用MGDF和RBF函数作为复数帧段输入HMM的输出概率分布函数的方法。通过对非特定人汉语孤立数字和连续数字语音识别试验,证实了该文提出的引入帧间相关信息方法的有效性。     

Spotting and Recognition of Consonant-Vowel Units from Continuous Speech Using Accurate Detection of Vowel Onset Points

In this paper, we propose an efficient approach to spotting and recognition of consonant-vowel (CV) units from continuous speech using accurate detection of vowel onset points (VOPs). Existing methods for VOP detection suffer from lack of high accuracy, spurious VOPs, and missed VOPs. The proposed VOP detection is designed to overcome most of the shortcomings of the existing methods and provide accurate detection of VOPs for improving the performance of spotting and recognition of CV units. The proposed method for VOP detection is carried out in two levels. At the first level, VOPs are detected by combining the complementary evidence from excitation source, spectral peaks, and modulation spectrum. At the second level, hypothesized VOPs are verified (genuine or spurious), and their positions are corrected using the uniform epoch intervals present in the vowel regions. The spotted CV units are recognized using a two-stage CV recognizer. Two-stage CV recognition system consists of hidden Markov models (HMMs) at the first stage for recognizing the vowel category of a CV unit and support vector machines (SVMs) for recognizing the consonant category of a CV unit at the second stage. Performance of spotting and recognition of CV units from continuous speech is evaluated using Telugu broadcast news speech corpus.     

从线性预测HMM到一种新的语音识别的混合模型

线性预测HMM(Linear Prediction HMM,LPHMM)并没有象传统HMM那样引入状态输出独立同分布假设,但实用中识别性能并不佳.通过分析两种HMM的各自优劣,本文提出了一种新的语音识别的混合模型,将语音静态特性(基于传统HMM)和动态特性(基于LPHMM)分别描述又有机结合在一起,更为精确地刻划了真实的语音现象,同时又继承使系统的实现改动很小和较小的计算量.汉语大词汇量非特定人连续语音识别的实验表明,混合模型的识别性能显著好于LPHMM和传统HMM.理论上,本文还给出了LPHMM的一组闭式参数重估公式.     

基于PCANN/HMM混合结构的语音识别方法

本文提出了一种基于PCANN/HMM混合结构的语音识别方法,它采用相继几帧组成的特征参数矢量作为语音识别HMM的输入,能有效地在语音识别HMM中引入帧间相关信息,同时为了改善多帧特征输入HMM的输出概率密度函数性能,在HMM的前端增加语音参数压缩的主分量分析神经网络(PCANN).通过对多讲者汉语连续语音识别实验,证实了本文提出方法的有效性.     

A fused hidden Markov model with application to bimodal speech processing

This paper presents a novel fused hidden Markov model (fused HMM) for integrating tightly coupled time series, such as audio and visual features of speech. In this model, the time series are first modeled by two conventional HMMs separately. The resulting HMMs are then fused together using a probabilistic fusion model, which is optimal according to the maximum entropy principle and a maximum mutual information criterion. Simulations and bimodal speaker verification experiments show that the proposed model can significantly reduce the recognition errors in noiseless or noisy environments.     

Upper Bound Kullback–Leibler Divergence for Transient Hidden Markov Models

This paper reports an upper bound for the Kullback–Leibler divergence (KLD) for a general family of transient hidden Markov models (HMMs). An upper bound KLD (UBKLD) expression for Gaussian mixtures models (GMMs) is presented which is generalized for the case of HMMs. Moreover, this formulation is extended to the case of HMMs with nonemitting states, where under some general assumptions, the UBKLD is proved to be well defined for a general family of transient models. In particular, the UBKLD has a computationally efficient closed-form for HMMs with left-to-right topology and a final nonemitting state, that we refer to as left-to-right transient HMMs. Finally, the usefulness of the closed-form expression is experimentally evaluated for automatic speech recognition (ASR) applications, where left-to-right transient HMMs are used to model basic acoustic-phonetic units. Results show that the UBKLD is an accurate discrimination indicator for comparing acoustic HMMs used for ASR.      

Isolated-utterance speech recognition using hidden Markov modelswith bounded state durations

Hidden Markov models (HMMs) with bounded state durations (HMM/BSD) are proposed to explicitly model the state durations of HMMs and more accurately consider the temporal structures existing in speech signals in a simple, direct, but effective way. A series of experiments have been conducted for speaker dependent applications using 408 highly confusing first-tone Mandarin syllables as the example vocabulary. It was found that in the discrete case the recognition rate of HMM/BSD (78.5%) is 9.0%, 6.3%, and 1.9% higher than the conventional HMMs and HMMs with Poisson and gamma distribution state durations, respectively. In the continuous case (partitioned Gaussian mixture modeling), the recognition rates of HMM/BSD (88.3% with 1 mixture, 88.8% with 3 mixtures, and 89.4% with 5 mixtures) are 6.3%, 5.0%, and 5.5% higher than those of the conventional HMMs, and 5.9% (with 1 mixture), 3.9% (with 3 mixtures) and 3.1% (with 1 mixture), 1.8% (with 3 mixtures) higher than HMMs with Poisson and gamma distributed state durations, respectively     

一种基于SDTS的HMM训练算法

用传统的BW算法训练语音识别系统的HMM需要大量的语音数据。本文在假设声学模型系统的子空间捆绑结构(SDTS)为己知的前提下,提出了一种新的训练算法,可以有效地减少系统对训练数据的需求。理论分析和仿真表明,与传统的BW算法比较,新的训练算法(IBW)可压缩模型参数15倍,从而可大量地减少训练数据。尽管新算法要用到系统的先验知识,但它还是显示了许多优越性。     

T-S型RBF神经网络在红外火焰探测系统中的应用

基于T-S(Takagi-Sugeno,高木-关野)模型RBF(Radial Basis Function,径向基函数)神经网络,提出了一种应用于三波段点型红外火焰探测器的识别算法,同时实现了硬件电路以及软件程序的设计。针对火焰探测器在检测过程中可能出现的数据丢失、失真、饱和等复杂情况,本文利用RBF网络较优的逼近精度和泛化能力,同时结合T-S模型用少量的模糊规则可生成较复杂的非线性函数的特点,实现了火焰与干扰源的准确识别。实验证实,T-S模型RBF神经网络相比于BP(Back Propagation,反向传播)网络在逼近精度、收敛速度、鲁棒性等多个方面都有所提升。     

基于多流三音素DBN模型的音视频语音识别和音素切分

为实现音视频语音识别和同时对音频视频流进行准确的音素切分,该文提出一个新的多流异步三音素动态贝叶斯网络(MM-ADBN-TRI)模型,在词级别上描述了音频视频流的异步性,音频流和视频流都采用了词-三音素-状态-观测向量的层次结构,识别基元是三音素,描述了连续语音中的协同发音现象.实验结果表明:该模型在音视频语音识别和对音频视频流的音素切分方面,以及在确定音视频流的异步关系上,都具备较好的性能.