SBIR Phase I: Automated Public Speaking Assessment

这个SBIR第一阶段的项目将研究自动口语评估的可行性，以帮助学生提高口语交流技能。根据对人力资源官员的一项调查，如今只有25%的大学毕业生拥有良好的演讲技能进入职场。这意味着许多人无法有效地说服听众相信他们的立场，从而限制了他们推销新想法和在工作中取得成功的能力。该项目将研究语音技术方面的新研究，使学生能够在任何时候收到客观的、个性化的反馈。通过自我调整进度的练习和反馈来加强沟通技能，用户将更好地为执行工作任务和进入领导角色所需的沟通技能做好准备。建成后的系统最初将提供给美国每年参加公开演讲培训的200多万人。这项技术未来的潜在应用包括教师评估、呼叫中心监控、面试培训、角色扮演、人力资源评估、病人护理、聋人服务、语言学习和学生评估。该项目将开发自动公开演讲评估的关键概念，以便能够客观地衡量学生的发声能力，并以创造独立、个性化学习体验的方式呈现。将听者感知与言语行为联系起来是语音自动评价的一个新方向。在利用自动语音识别和语义分析的口语熟练程度领域，已经展示了对语音的自动评估。自动语音评估也被用于测谎和情绪检测，重点是用户声音中的自主神经反应，例如当压力影响声带时。SBIR项目背后的假设是，软件可以帮助说话人有意识地使用和修改非语义言语行为，以产生更令人满意的听者感知。第一阶段的目标是确定可用于预测受众感知的语音的关键特征，并开发初始软件模型来估计受众感知的各个方面。为了实现这些目标，将应用专家特征列举、深度学习特征识别和机器学习的组合，并在大量演员声音和人类感知评级的语料库中进行迭代测试。

This SBIR Phase I project will study the feasibility of automated speaking assessment to help students improve their oral communication skills. According to a survey of human resource officials, only 25 percent of today's college graduates enter the workforce with well-developed speaking skills. This means many people are unable to effectively persuade an audience of their position, thus limiting their ability to sell new ideas and be successful in their jobs. The project will investigate novel research in speech technology that enables students to receive objective, personalized feedback at any time. By reinforcing communication skills through self-paced practice and feedback, users will be better prepared with the communications skills necessary to perform job tasks and move into leadership roles. The completed system will initially be offered to over two million people in the United States who participate in public speaking training annually. Potential future applications for the technology include teacher assessments, call center monitoring, interview training, role playing, human resources assessments, patient care, services for the deaf, language learning and student assessments.This project will develop key concepts for automated public speaking assessment such that a student's vocal delivery can be objectively measured and presented in a manner that creates an independent, personalized learning experience. Linking listener perceptions to speech behaviors is a novel direction in automated assessment for speech. Automated assessment for speech has already been demonstrated in the area of spoken language proficiency, which leverages automated speech recognition and semantic analysis. Automated voice assessment has also been utilized in lie detection and emotion detection, which focus on autonomic responses in the user's voice, such as when stress affects the vocal cords. The hypothesis behind this SBIR project is that software can help speakers to consciously use and modify non-semantic speech behaviors to produce more desirable listener perceptions. The Phase 1 objectives are to identify key features of voice that can be used to predict audience perception and develop initial software models to estimate aspects of audience perception. To achieve these objectives, a combination of expert feature enumeration, deep learning feature identification and machine learning will be applied and iteratively tested against a large corpus of actor voices and human perception ratings.