EAGER: ATAROS: Automatic Tagging and Recognition of Stance

从安排会议这样简单的活动到平衡国家预算这样复杂的活动，人们在谈判和决策中都有自己的立场。虽然主观性和情感分析的相关领域受到了极大的关注，但工作几乎完全集中在文本上，而许多采取立场的活动是口头进行的。早期的实验表明，当人们采取立场时，他们会改变自己的说话风格，听众通过听原话比阅读抄本更容易发现消极态度。然而，由于影响语音产生的因素的多样性，从个体差异到社会背景，隔离语音中的立场信号以进行自动识别提出了巨大的挑战。这个探索性研究项目的早期资助代表了对口语互动的重点探索，以提供与立场采取相关的语言因素的特征，并开发利用这些特征来自动检测立场采取行为的计算方法。通过对金融危机国会听证会的受控引出和存档录音的分析，确定了立场的强大语言标记。前者允许实验比较来突出有时微妙的对比，而后者可以在现实世界的高风险讨论中验证和扩展这些发现。该分析包括对语音动态模式的新颖声学-语音测量，如元音空间缩放和音高/能量速度，以及为支持特征探索而开发的复杂可视化技术。研究结果通过结合声音和词汇线索的姿态识别实验得到验证，为自动跟踪态度的趋势和转变奠定了基础。

From activities as simple as scheduling a meeting to those as complex as balancing a national budget, people take stances in negotiations and decision making. While the related areas of subjectivity and sentiment analysis have received significant attention, work has focused almost exclusively on text, whereas much stance-taking activity is carried out verbally. Early experiments suggest that people alter their speaking style when engaged in stance-taking, and listeners can much more readily detect negative attitudes by listening to the original speech than by reading transcripts. However, due to the diversity of factors that influence speech production, from individual differences to social context, isolating the signals of stance-taking in speech for automatic recognition presents substantial challenges.This Early Grant for Exploratory Research project represents a focused exploration of spoken interactions to provide a characterization of linguistic factors associated with stance-taking and develop computational methods that exploit these features to automatically detect stance-taking behavior. Robust linguistic markers of stance-taking are identified through analysis of both controlled elicitations and archived recordings of Congressional hearings on the financial crisis. The former allow experimental comparisons to highlight sometimes subtle contrasts, while the latter enable validation and extension of those findings in real-world, high-stakes discussions. The analysis includes novel acoustic-phonetic measures of dynamic patterns in speech, such as vowel space scaling and pitch/energy velocity, with sophisticated visualization techniques developed to support feature exploration. Findings are validated via stance recognition experiments combining acoustic and lexical cues, which lay the foundation for automatic tracking of trends and shifts in attitudes.