基于嵌入式Linux的矩阵键盘驱动程序研究与开发

主要介绍基于嵌入式Linux的矩阵键盘驱动程序设计的方法,硬件平台基于TI提供的OMAP5912构建的嵌入式语音识别系统,充分利用OMAP5912的外围硬件资源,矩阵键盘作为平台设备和输入设备,利用Linux内核提供的输入子系统.输入子系统为输入设备驱动开发提供了良好的接口,提高了驱动程序的开发效率.驱动开发完成后,在MiniGui和Qtopia下测试,结果证明驱动程序工作高效、稳定.     

一种支持PCIe和PCI主机接口的模块测试平台设计

为满足外场种类繁多的具有标准PCIe和PCI主机接口的FC网络接口模块技术服务保障需求,文章设计了一种支持PCIe和PCI主机接口的FC网络接口模块测试平台,详细介绍了测试平台PCIe接口、PCI接口和FC接口的设计方案,测试验证和工程应用表明,该接口模块测试平台各项功能均满足设计要求.     

基于DSP的通用语音编译码器设计与实现

实现了一种基于DSP的通用语音编译码器实验平台。在该平台上通过软件重构可以实现常用语音信号的编译码。详细介绍了应用DSP实现通用语音编译码中的关键技术,包括硬件平台设计、软件结构和接口扩展等。由于硬件和软件采用模块和结构化设计,通过开放的模块接口和子程序使得该设计具有扩展和升级容易、学生参与度高等优点。最后通过实际测试验证了设计的正确性。     

HMM在语音识别系统中的应用

介绍语音识别技术的应用状况与发展,对基于动态时间伸缩技术、隐含马尔科夫模型及人工神经网络的3种不同的语音识别系统进行了比较,重点介绍了隐含马尔科夫模型(HMM)在语音识别系统中的应用。其中基于HMM的语音识别系统是在UniSpeech芯片上实现基于DHMM的识别系统,然后又在同一平台上实现了基于CHMM的识别系统。     

基于PXI的水声信号采集和回放模块设计

介绍了采用PCI专用芯片和CPLD实现PXI接口功能的水声信号采集和回放模块设计方法。该模块包括接口部分和功能电路部分,能够完成1路采集和4路回放。其中PXI总线负责各种芯片的初始化和控制,并通过测试系统反馈的数据监测整个测试过程。CPLD完成DDS等器件的时序和PCI接口芯片控制等。该模块不仅可用于多普勒速度声纳的测试,同时也可以作为通用的波形采集和回放模块,用于声纳系统陆上仿真平台等。     

基于SOPC技术的数字音频处理系统设计

介绍了一种基于语音编解码芯片TLV320AIC23和可编程片上系统(SOPC)技术的嵌入式数字音频处理系统设计方案,通过在Altera公司的CycloneⅡFPGA上配置NiosⅡ软核处理器、语音芯片TLV320AIC23的I2C配置模块、数字音频处理模块等相关接口模块来搭建硬件平台,并利用软件集成开发环境NiosⅡIDE进行软件设计来控制整个系统工作。最后对整个系统进行了调试,实验结果表明系统实现了数字音频的高速采集、存储及回放等功能。     

一个基于DTW算法的语音识别系统

本文在ＷＩＮＤＯＷＳ９８平台上－基于ＤＴＷ算法，实现了６４个汉族词汇的语音识别系统。文章阐述了整个语音识别系统原理和算法的实现过程，并对其中一些算法进行了进行了改进，对如何进一步提高系统的性能作了许多探讨。     

基于WinCE下NDIS小端口驱动程序的设计与实现

介绍了在Windows CE平台下依赖网络驱动接口规范(NDIS)如何开发NDIS小端口驱动程序,包括Windows CE系统的整个网络结构以及各个模块之间的通讯.     

数字语音处理软件及其接口电路设计

语音模块是一些数字终端的重要组成部分。文中讨论了笔者研发的语音模块中数字语音处理软件及其接口电路设计，涉及到语音数据的压缩与解压、接口电路及信今系统的开发，以及多主流程软件设计等方面的内容。     

SPI在TD-SCDMA/GSM双模移动开发平台中的应用

本文简要介绍了基于软件无线电技术的新型TD-SCDMA/GSM双模移动终端开发平台,该平台中的主要模块及FPGA作为协处理器在该平台中实现的功能。根据SPI的协议标准和在该平台中的通信协议要求,介绍了SPI在双模开发平台中的实现应用方法。设计中采用Verilog HDL语言并在Xilinx公司的Virtex-Ⅱ系列FPGA芯片内成功模拟出一个SPI接口,其仿真测试和实际应用均完全符合此双模平台通信的要求。     

Gaze-contingent automatic speech recognition

There has been progress in improving speech recognition using a tightly-coupled modality such as lip movement; and using additional input interfaces to improve recognition of commands in multimodal human? computer interfaces such as speech and pen-based systems. However, there has been little work that attempts to improve the recognition of spontaneous, conversational speech by adding information from a loosely?coupled modality. The study investigated this idea by integrating information from gaze into an automatic speech recognition (ASR) system. A probabilistic framework for multimodal recognition was formalised and applied to the specific case of integrating gaze and speech. Gaze-contingent ASR systems were developed from a baseline ASR system by redistributing language model probability mass according to the visual attention. These systems were tested on a corpus of matched eye movement and related spontaneous conversational British English speech segments (n = 1355) for a visual-based, goal-driven task. The best performing systems had similar word error rates to the baseline ASR system and showed an increase in keyword spotting accuracy. The core values of this work may be useful for developing robust speech-centric multimodal decoding system functions.     

Spracherkennung mit Hidden Control Neural Networks

Hidden control neural networks (HCN networks) are suitable for a variety of pattern recognition techniques. The speech recognizer described here is built for speaker-independent single-word recognition and is intended to implement user interfaces to control devices via simple word-commands. To evaluate the speech recognizer, it has been applied to minimum pairs. Within a minimum pair two words differ only in a single phoneme. It was achieved to increase the recognition rate while taking those periods of time especially into account, that are found to contain the relevant difference.     

一种语音特征参数子分量分析与有效性评价的新方法

语音信号中包含语义和说话人个性两大特征,其有效提取和强化对语音识别和说话人识别有着非常重要的意义。本文提出了一种语音特征参数中语义和个性特征子分量分析与有效性评价的4S方法,对语义和个性特征的成份比例进行分析,并通过量化指标评判特征参数对语音识别和说话人识别的有效性。运用4S分析方法对目前常用的特征参数LPC, LPCC和MFCC的子分量分析与有效性评价结果表明,所有的特征参数都更多地包含了语义特征信息,语义特征和说话人个性特征的成份比例因子LIR分别为1．30、1．44和1．61,并且,三种参数对语音识别和说话人识别的有效性均呈现出依次提高的特性。     

Application of Emotion Recognition and Modification for Emotional Telugu Speech Recognition

Majority of the automatic speech recognition systems (ASR) are trained with neutral speech and the performance of these systems are affected due to the presence of emotional content in the speech. The recognition of these emotions in human speech is considered to be the crucial aspect of human-machine interaction. The combined spectral and differenced prosody features are considered for the task of the emotion recognition in the first stage. The task of emotion recognition does not serve the sole purpose of improvement in the performance of an ASR system. Based on the recognized emotions from the input speech, the corresponding adapted emotive ASR model is selected for the evaluation in the second stage. This adapted emotive ASR model is built using the existing neutral and synthetically generated emotive speech using prosody modification method. In this work, the importance of emotion recognition block at the front-end along with the emotive speech adaptation to the ASR system models were studied. The speech samples from IIIT-H Telugu speech corpus were considered for building the large vocabulary ASR systems. The emotional speech samples from IITKGP-SESC Telugu corpus were used for the evaluation. The adapted emotive speech models have yielded better performance over the existing neutral speech models.

     

User-centered modeling and evaluation of multimodal interfaces

Historically, the development of computer interfaces has been a technology-driven phenomenon. However, new multimodal interfaces are composed of recognition-based technologies that must interpret human speech, gesture, gaze, movement patterns, and other complex natural behaviors, which involve highly automatized skills that are not under full conscious control. As a result, it now is widely acknowledged that multimodal interface design requires modeling of the modality-centered behavior and integration patterns upon which multimodal systems aim to build. This paper summarizes research on the cognitive science foundations of multimodal interaction, and on the essential role that user-centered modeling has played in prototyping, guiding, and evaluating the design of next-generation multimodal interfaces. In particular, it discusses the properties of different modalities and the information content they carry, the unique features of multimodal language and its processability, as well as when users are likely to interact multimodally and how their multimodal input is integrated and synchronized. It also reviews research on typical performance and linguistic efficiencies associated with multimodal interaction, and on the user-centered reasons why multimodal interaction minimizes errors and expedites error handling. In addition, this paper describes the important role that selective methodologies and evaluation metrics have played in shaping next-generation multimodal systems, and it concludes by highlighting future directions for designing a new class of adaptive multimodal-multisensor interfaces.     

神经网络与HMM构成的混合网络在语音识别中应用的研究

隐马尔可夫模型（ＨＭＭ）技术是语音识别中应用较为成功的算法，但它的缺点影响了其精度、速度、硬件实现和推广应用。神经网络（ＮＮ）具有并行性、强的分类能力和易于硬件实现等优点。将ＮＮ与ＨＭＭ相结合构成混合网络，能克服ＨＭＭ与ＮＮ的缺点，保留双方的优点。本文详细评述了目前在语音识别中应用的由ＨＭＭ和ＮＮ构成的四种混合网络。通过对其结构、识别性能和特点的分析，可以看出ＨＭＭ和ＮＮ构成的混合网的性能明显优于     

VoiceXML and the W3C speech interface framework

VoiceXML is a markup language for creating voice-user interfaces. It uses speech and telephone touchtone recognition for input and prerecorded audio and text-to-speech synthesis (TTS) for output. It's based on the World Wide Web Consortium's (W3C's) Extensible Markup Language (XML) and leverages the Web paradigm for application development and deployment. By having a common language, application developers, platform vendors, and tool providers all can benefit from code portability and reuse. The paper discusses VoiceXML and the W3C speech interface framework.     

嵌入式语音控制选单系统的实现与应用

采用ARM处理器$3C2440A构建嵌入式系统，利用音频芯片UDA1341TS对语音信号进行编解码，应用语音识别技术实现语音控制。介绍了系统设计原理和工作机制，并阐述了控制选单的软硬件设计方案和识别算法原理，给出测试方法。实验结果表明，系统能实现特定指令的语音控制，识别率高、实时性好，可适应复杂的工作环境。     

A Tool for Interactive Advice on the Use of Speech in Multimodal Systems

With the recent spread of speech technologies and the increasing availability of application program interfaces for speech synthesis and recognition, system designers are starting to consider whether to add speech functionality to their applications. The questions that ensue are by no means trivial. SMALTO, the tool described below, provides advice on the use of speech input and/or output modalities in combination with other modalities in the design of multimodal systems. SMALTO (S peech M odality A uxi L iary TO ol), implements a theory of modalities and incorporates structured data extracted from a corpus of claims about speech functionality found in recent literature on multimodality. The current version of the system aims mainly at supporting decisions at early design stages, as a hypertext system. However, further uses of SMALTO as part of a complete domain-oriented design environment are also envisaged.