AUGMENTATIVE COMMUNICATION UTILIZING SPEECH RECOGNITION

感知到的会话能力是决定社交、学术和职业成功的重要因素。对于需要辅助技术才能有效沟通的人来说，缓慢的信息产生可能会限制这些领域的进步。许多旨在加速通信的增强技术依赖于统计单词预测方法。这项拟议的研究调查了使用语音识别来捕获由健全的熟人提供的对话上下文的方法，以改进对增强的熟人的单词预测。快速变化的话题和对话风格对单个单词的概率产生了深远的影响。诸如单词新近性、单词触发器和语篇领域识别之类的技术允许统计语言模型利用变化的上下文来提供基本上更准确的预测。在拟议工作的第一阶段，这些方法将应用于转录的对话，以建立基线绩效衡量标准。为了估计识别错误的影响，这些研究将使用经过仔细调整的语音识别错误模拟器人工降级的转录来重复进行。最后，商业语音识别引擎将集成到测试软件中。这个原型系统的可行性将通过将其应用于增强的通信者和健全的对话者之间的现实对话来建立。拟议的商业应用：拟议的研究以增强通信者的全部人口为目标，特别强调具有极慢的交流速度(每分钟少于5个单词)和良好的语言技能的人。虽然这些技术将与内部AAC系统一起开发，但它们可以推广应用于第三方通信设备。

Perceived conversational competency is an important factor in determining social, academic, and professional success. For persons who require assistive technology to communicate effectively, slow message production can limit advancements in these areas. A number of augmentative techniques designed to accelerate communication rely upon statistical word prediction methods. The proposed study investigates methods of using speech recognition to capture the conversational context provided by the able-bodied conversant to improve word prediction for the augmented conversant. The rapidly changing topic and style of a conversations has a profound impact on the probability of individual words. Techniques such as word recency, word triggers, and discourse domain identification allow statistical language models to exploit shifting context to provide substantially more accurate predictions. In the first stage of the proposed effort, these methods will be applied to transcribed conversations to establish baseline performance measures. To estimate the impact of recognition errors, the studies will be repeated using transcriptions that have been artificially degraded using a carefully tuned speech recognition error simulator. Finally, a commercial speech recognition engine will be integrated into the testing software. The feasibility of this prototype system will be established by applying it to realistic conversations between augmented communicators and able-bodied interlocutors. PROPOSED COMMERCIAL APPLICATIONS: The proposed research is targeted at the entire population of augmented communicators, with special emphasis on persons with extremely slow communication rates (less than 5 words per minute) and good language skills. Although the technologies will be developed with an inhouse AAC system, they can be generalized for application to third-party communication devices.