RI: Small: Collaborative Research: 'Houston We Have A Solution': Novel Speech Processing Advancements for Analysis of Large Asynchronous Multi-Channel Audio Corpora

RI：小型：协作研究：“休斯顿，我们有一个解决方案”：用于分析大型异步多通道音频语料库的新颖语音处理进步

• 批准号: 1219130
• 负责人: John Hansen
• 金额: $ 36.52万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1219130&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Houston

This project is focused on developing new speech processing techniques which will transform access to large asynchronous multi-channel and diverse collections of multimedia materials. In particular, the algorithms developed are being employed to create a novel multi-source and multi-scale event reconstruction system that brings together the massive archives of the Apollo lunar missions, to create experiential interaction with historical materials. Specific research advancements are focused on state of the art acoustic environment analysis, speech recognition including keyword spotting, speaker identification under adverse conditions, multimodal content alignment, and automated linking for events and entities from spoken content. Specifically, the research is developing: (i) new techniques for noise- and channel-robust acoustic processing, exploiting missing-features concepts with novel feature extraction and compensation techniques, (ii) a new articulatory framework for speech recognition for robustness to variations in speech production, (iii) environmental "sniffing" techniques to automatically characterize acoustic environments to improve robustness, and (iv) automatic detection of novel task-specific audio-events. Since the data is asynchronous, unique speech analytics techniques are being formulated to address the large number of "local loop" intercom circuits in the NASA Mission Control Center, audio recorded onboard the two Apollo spacecrafts during specific mission events, and space-to-ground radio circuits. The specific speech, language, and knowledge extraction advancements will be integrated into a new automated evaluation model that reflects specific challenges encountered in the event reconstruction task. This platform will be deployed and evaluated by actual users from the Science and Engineering Education Center (SEEC) of the University of Texas at Dallas. Integration of robust speech processing algorithms with event reconstruction systems will have a direct and immediate impact on education, society, and government organizations. Working with NASA's Apollo mission data allows for the development of speech technology for challenging audio that contains severe communication channel artifacts, cross-talk/static/tones, and low signal-to-noise ratios. The software being developed in this project will be made available to any non-profit organization for use in audio/video search (download with training modules). Students working on senior design teams will also develop a Contact Science station to be deployed in Dallas, TX and overseen by the University of Texas in Dallas Science and Engineering Education Center to illustrate and assess student use of the advancements. As a lasting legacy for this project, this project team includes eminent historians of human space flight, who will explore opportunities to deploy this event reconstruction system in a museum setting where it can support both scholarship and public engagement, and we will make the system itself available on an open-source basis to support other researchers.

这个项目的重点是开发新的语音处理技术，这将改变对大型异步多通道和各种多媒体材料集合的访问。特别是，所开发的算法正被用于创建一个新的多源、多尺度事件重建系统，该系统将阿波罗登月任务的大量档案汇集在一起，以创造与历史材料的体验互动。具体的研究进展集中在最先进的声学环境分析、语音识别（包括关键字识别）、不利条件下的说话人识别、多模态内容对齐以及从语音内容中自动链接事件和实体。具体来说，研究正在开发：(i)噪声和通道鲁棒声学处理的新技术，利用新颖的特征提取和补偿技术利用缺失特征概念，（ii）语音识别的新发音框架，对语音产生的变化具有鲁棒性，（iii）环境“嗅探”技术，自动表征声学环境，以提高鲁棒性，以及（iv）自动检测新的任务特定音频事件。由于数据是异步的，因此正在制定独特的语音分析技术，以解决NASA任务控制中心大量的“本地环路”对讲电路，在特定任务事件期间在两个阿波罗航天器上录制的音频，以及空间对地无线电电路。具体的语音、语言和知识提取进展将集成到一个新的自动化评估模型中，该模型反映了事件重建任务中遇到的具体挑战。该平台将由德克萨斯大学达拉斯分校科学与工程教育中心（SEEC）的实际用户进行部署和评估。鲁棒语音处理算法与事件重建系统的集成将对教育、社会和政府组织产生直接和直接的影响。与NASA的阿波罗任务数据合作，可以开发语音技术，以应对包含严重通信通道伪影、串扰/静态/音调和低信噪比的音频挑战。本项目正在开发的软件将提供给任何非营利组织用于音像搜索（随培训模块下载）。在高级设计团队工作的学生还将开发一个接触科学站，部署在德克萨斯州达拉斯，由德克萨斯大学达拉斯科学与工程教育中心监督，以说明和评估学生对这些进步的使用。作为这个项目的持久遗产，这个项目团队包括著名的人类太空飞行历史学家，他们将探索在博物馆环境中部署这个事件重建系统的机会，在那里它可以支持学术和公众参与，我们将使系统本身在开源的基础上可用，以支持其他研究人员。