Automatic speech recognition for mobile and personal applications

在这个提议中，我们研究了如何提高自动语音识别（ASR）的性能，它将普通语音转换为文本。这使得无人监督的程序可以通过语音与人互动，从而使人们可以通过电话或PDA（个人数字助理）获得广泛的数据服务。ASR现在只存在于有限的服务中，因为它的准确性远远低于人类听众。为了让ASR得到广泛的使用，我们需要让它更高效。该提案涉及几个目标关注领域，例如，通过无线信道访问数据库。这项研究涉及智能信号处理，因为我们需要开发算法来解释语音信号的意图。与人类完成的复杂的非线性处理相比，当前ASR中使用的模型简单，这表明还有很多方法可以改善ASR。在每个级别（即，特征估计、时间和声学建模、语言建模、决策），在ASR中已经做出妥协以具有简单和快速的处理，代价是较低的识别准确度。我们打算研究这些妥协和替代方法来接近人类语音识别的性能。我们工作的一个主要应用是通过语音呈现搜索和访问信息，例如，在万维网上发现的大型数据库。在小型便携式设备上打字很笨拙，因此通过语音进行访问是非常必要的。人机交互通常使用鼠标+键盘作为机器输入，计算机屏幕或打印机作为输出。然而，言语在人类交流中一直占有很高的优先地位，早在书写之前就发展起来了。虽然在通信带宽上不如文本经济，但语音比文本处理更通用和易于使用，例如，人们说话比打字更快更容易。建议的researchhere涉及改进ASR，使其更实用，因此更有用。在recherche nouvelles methodes afin d'amélibrary la performance de systèmes de reconsideration automatiquede parole（RAP），ce qui convertit la parole ordinaire en texte. Ceci permet aux algorithmes non dirigés àinteragir avec les gens par la sporel，ce qui quidonne accès à une dégamme de services de données，partéléphone ou par assistants numériques.

In this proposal, we examine ways to improve the performance of automatic speech recognition (ASR), whichconverts ordinary speech into text. This allows unsupervised programs to interact with people via voice, thusgiving people access to a broad range of data services, over telephones or PDAs (personal digital assistants).ASR exists now only for limited services, as its accuracy is much lower than that of human listeners. For ASRto gain widespread use, we need to make it much more efficient. This proposal addresses several areas oftargeted interest, e.g., accessing databases over wireless channels. The research deals with intelligent signalprocessing, as we need to develop algorithms to interpret voice signals in terms of their intent.The simplicity of models used in current ASR, when compared to the complex, nonlinear processing done byhumans, suggests that there remain many ways to improve ASR. At each level (i.e., feature estimation,temporal and acoustic modeling, language modeling, decision making), compromises have been made in ASRto have simple and fast processing, at the expense of lower recognition accuracy. We intend to examineseveral of these compromises and alternative ways to approach the performance of human speech recognition.A major application of our work is to render search and access to information via voice, e.g., large databasesas found on the World Wide Web. Typing on small portable devices is awkward; thus access via voice ishighly desired.Human-computer interactions often use a mouse+keyboard as machine input, and a computer screen orprinter as output. Speech, however, has always had a high priority in human communication, developed longbefore writing. While less economical in communication bandwidth than text, speech is more universal andeasy to use than text processing, e.g., people speak more quickly and easily than typing. The proposed researchhere deals with improving ASR to render it more practical and hence more useful.On recherche nouvelles méthodes afin d'améliorer la performance de systèmes de reconnaissance automatiquede parole (RAP), ce qui convertit la parole ordinaire en texte. Ceci permet aux algorithmes non dirigés àinteragir avec les gens par la parole, ce qui leur donne accès à une grande gamme de services de données, partéléphone ou par assistants numériques.