SBIR Phase I: Say What I Feel

这个SBIR第一阶段项目旨在创建一个基于情感表达软件的语音生成交流系统，该系统专为很少或没有语言交流能力的人设计，特别是那些患有自闭症谱系障碍的人。目前的替代通信技术不提供表达情感内容的能力，而情感内容对于在家庭、社区和工作中进行有效、可理解的沟通至关重要。通过语调或面部表情来表达情感——比如悲伤、愤怒或快乐——的能力对有沟通障碍的人来说具有深远的教育和职业意义。换句话说，情感内容有助于澄清口头信息背后的交流意图。当非语言个体能够进行彻底而有效的交流时，他们就增加了接受免费而适当的公共教育的能力，并扩大了他们的职业机会。当个人能够接受教育并找到工作时，他们就可以回馈社会。建议的工作支持科学和工程的进步，但它也增强了当前教育应用和未来研究的潜力。最后，该项目以联邦和各州为残疾人提供教育和职业服务的努力为基础。该项目将开发并结合针对数字面部识别模式和基于语音的波形的新型软件算法，以增强无法自主说话的个体的对话交流中的情感内容。在合成语音中添加情感内容的能力是一项新技术，可以极大地造福于有沟通障碍的人，比如自闭症谱系障碍患者。该项目旨在促进和增强情感表达，从而提高使用基于软件的合成语音系统来表达他们的想法、感受和愿望的个人的沟通能力。要衡量这个项目是否能准确地通过言语和面部表情传达人类情感，需要对神经正常的成年人进行感知测试。具体来说，知觉测试将包括受过训练和未受过训练的法官。这样做的目的是确保面部表情以及音高轮廓和声音重音与情绪标签（即快乐，悲伤或疯狂）保持一致。为了准确地计算评委之间的可信度，这项研究将使用一种方法来确定评委的评分一致的程度，相对于他们可能会在多大程度上一致，如果他们随机对相同的刺激进行评分。

This SBIR Phase I project aims to create an emotionally expressive software-based speech-generating communication system, which is designed for individuals with little or no verbal communication ability -- particularly those with autism spectrum disorder. Current alternative communication technology does not offer the ability to express emotional content: something that is crucial to effective, comprehensible communication at home, in the community, and at work. The ability to convey emotions -- sadness, anger, or happiness, for example -- through one's tone of voice or facial expressions has far-reaching educational and vocational benefits for individuals with communication challenges. In other words, emotional content helps clarify the communicative intent behind a spoken message. When non-verbal individuals can communicate thoroughly and effectively, they increase their ability to receive a free and appropriate public education, and they expand their vocational opportunities. When individuals can receive an education and find a job, they can contribute back to society. The proposed work supports progress in science and engineering, yet it also enhances the potential for current educational applications and future research studies. Finally, this project builds on Federal and State efforts to offer people with disabilities educational and vocational services. This project will develop and combine novel software algorithms that target digital facial recognition patterns and speech-based waveforms, in order to enhance the emotional content of conversational communication for individuals who cannot speak volitionally. The ability to add emotional content onto synthesized speech is a new technology that could substantially benefit individuals with communication challenges, such as people with autism spectrum disorder. This project seeks to facilitate and augment emotional expression and, therefore, increase communicative competence for individuals who use software-based synthesized speech systems to express their ideas, feelings, and wishes. Measuring whether or not this project accurately conveys human emotion in speech and facial expressions requires a perceptual test involving neurotypical adults. Specifically, the perceptual test will include both trained and untrained judges. The purpose of this is to ensure that the facial expressions as well as the pitch contour and vocal emphasis align with the emotion labels (i.e., happy, sad, or mad). In order to accurately calculate the judges' inter-rater reliability, the study will use a method that determines the extent to which judges' ratings agree, relative to how much they would likely conform, if they were to randomly rate the same stimuli.