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.

该项目的重点是开发新的语音处理技术，这些技术将改变对大型异步多渠道和多媒体材料集合的访问。特别是，正在采用开发的算法来创建一种新型的多源和多尺度事件重建系统，该系统汇集了Apollo Lunar任务的大量档案，以与历史材料建立体验互动。具体的研究进步集中在最先进的声学环境分析，语音识别，包括关键字发现，不利条件下的说话者识别，多模式内容对齐以及对口语内容的事件和实体的自动链接。具体而言，这项研究正在进行中：（i）用于噪声和频道稳定声学处理的新技术，通过新颖的特征提取和补偿技术利用缺失的功能概念，（ii）一种新的言语识别框架，以识别言论的鲁棒性，以对言语生产的变化进行稳健性，（iii）环境“嗅探”的环境，以自动化的环境，以自动化为特征，以使环境表现出各种自动化的环境。新颖的特定任务音频事件。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任务控制中心中大量的“本地环”对讲机，因此在特定的任务事件和空间到空间无线电通道的两个阿波罗航天台上记录了音频。具体的语音，语言和知识提取的进步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特定挑战。该平台将由得克萨斯大学达拉斯分校的科学与工程教育中心（SEEC）的实际用户进行部署和评估。强大的语音处理算法与事件重建系统的整合将对教育，社会和政府组织产生直接和直接的影响。使用NASA的Apollo任务数据，可以开发语音技术，以挑战音频，其中包含严重的通信渠道伪像，跨对词/静态/音调和低信噪比。该项目中正在开发的软件将提供给任何非营利组织用于音频/视频搜索（使用培训模块下载）。从事高级设计团队的学生还将开发一个联络科学站，将在德克萨斯州达拉斯部署，并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进步的使用。 作为该项目的持久遗产，该项目团队包括人类太空飞行的著名历史学家，他们将探索在博物馆环境中部署该活动重建系统的机会，在该博物馆环境中，它可以支持奖学金和公众参与，我们将以开放式制度为基础提供该系统本身以支持其他研究人员。