System for recording and analyzing telephone conversations between humans and artificial intelligence

评估言语和语言是一个昂贵而费力的过程，考虑到加拿大迅速变化的人口结构，这是完全不可持续的。除了评估的直接成本外，间接成本还包括旅行损失的时间、等待时间和评估所花费的时间，这是一个非常费力的过程，往往每隔几年才重复一次。从评估儿童自闭症到评估老年人阿尔茨海默病（AD）和痴呆症，整个人群都是如此。可重复、远程和可靠的评估至关重要。 许多人的语言理解力、词汇量和语言流畅性下降。我们的团队最近开发了计算方法，使用先进的机器学习，在识别原发性进行性失语症方面的准确率高达100%，在识别自发言语中的AD方面的准确率超过81%。由于语言的差异可以如此表明认知差异，我们有机会建立独特的工具，可以有效地远程监控语音。我建议在公共电话网络上使用1-800号码可访问的电话服务器来改进和评估这项技术。该系统将自动接收来自（和拨打）个人的电话，并完全自动化基于语音的测试，包括回答问题，叙述，重复语音和随机项目生成（RIG）。 有没有电话研究系统在部门（或在大学，我们知道），记录语音数据通过电话，也没有一个从事人机语音交互。这些资金将用于购买Falcon NorthWest计算服务器。它有256 GB的RAM -我们组中没有服务器有这个容量，但随着我们的需求随着数据的大小而增长，它变得越来越必要，这将需要超过2 TB的（备份）存储。由于机器学习将采用现代“深度学习”方法，因此还需要购买支持CUDA的NVIDIA显卡，这可以加速处理。 我们将使用现有的电话线来接收我们建议的系统的电话。但是，将电话线连接到计算机服务器需要特殊的电话硬件。我们需要在半长PCI或PCI Express（PCI-e）总线外形规格上的F环路启动（FXO）和/或工作站（FXS）端口，该类型由VoiceTronix提供。我们为这个完整的解决方案预算了1376美元。

Assessing speech and language is an expensive and laborious process that is completely unsustainable given Canada’s rapidly changing demographics. In addition to direct costs of assessment, indirect costs include lost time in travel, wait times, and hours spent in assessment, which is a process so laborious that it is often repeated only every few years. This is the case across the population, from assessing children for autism, to assessing older adults for Alzheimer's disease (AD) and dementia. Repeatable, remote, and reliable assessment is essential. Many individuals have diminished language comprehension, vocabulary, and speech fluency. Our team has recently developed computational methods that are up to 100% accurate in identifying primary progressive aphasia, and over 81% accurate in identifying AD from spontaneous speech, using advanced machine learning. Because differences in language can be so indicative of cognitive differences, we have an opportunity to build unique tools that can effectively monitor speech remotely. I propose to improve and evaluate this technology over the public phone network using a telephony-enabled server accessible by a 1-800 number. This system will automatically receive calls from (and make calls to) individuals, and completely automate speech-based tests, including question-answering, narratives, repeated speech, and random item generation (RIG). There is no telephony research system in the Department (or in the University that we know of) that records speech data over the phone, nor one that engages in human-computer speech interaction. These funds will pay for a Falcon NorthWest computational server. It has 256GB of RAM - no server in our group has this capacity, but it has become increasingly necessary, as our requirements grow with the size of our data, which will need over 2 TB of (backed up) storage. Since machine learning will employ modern ‘deep learning’ methods, it will also be necessary to purchase CUDA-enabled NVIDIA graphics cards, which can accelerate processing. We will use existing phone lines to receive phone calls to our proposed system. However, connecting the phone lines to the computer server requires special telephony hardware. We require F loop-start (FXO) and/or station (FXS) ports on a half length PCI or PCI Express (PCI-e) bus form factor, of the type made available by VoiceTronix. We are budgeting $1376 for this complete solution.