Conversational AI Audiology: Remote, natural and automated testing of hearing and fitting

会话式人工智能（AI），即患者可以与之交谈的技术，有可能消除获得医疗保健的障碍，特别是听力医疗保健。想象一下，担心听力问题的人不需要去看全科医生或听力学家进行首次诊断，而只需问他们的家庭助理“请检查我的听力”。这可能会导致15亿听力损失患者（到2050年将达到25亿）对听力损失的认识和助听器的使用。据估计，英国至少有600万人将受益于助听器，但只有200万人拥有助听器，而且他们的接受程度低且缓慢。与传统技术相比，与技术设备进行对话的听力测试有更多的优势：首先，它测试的是真实世界的声音（语音），而不是微弱的合成声音（听音图中的纯音）的可听性。其次，对话式人工智能系统可以直接模拟通过助听器发出的语音，从而量化最佳助听器设置所带来的好处。第三，远程检测不仅消除了获得医疗保健的障碍，而且对弱势患者也有好处。在本项目中，我们将文本到语音（TTS）和自动语音识别（ASR）应用于听力测试，以实现这些目标。TTS和ASR将使患者能够轻松地与测试系统进行交互，这对于那些难以使用图形用户界面的患者，特别是一些老年患者来说是一个额外的好处。患者和人工智能之间的交流将受到目标的驱动，即比传统测试更自然的互动，并在短时间内表征听力损失。候选人应对医疗保健感兴趣，并具有深度学习和其他人工智能领域的背景，获得工程、计算机科学、数学、物理或类似专业的学士学位。候选人将掌握TTS， ASR和主动学习的技能。与其他年轻研究人员的合作以及诸如期刊俱乐部之类的活动将有助于建立听力学和临床测试的基础知识。候选人将在机器学习会议（NeurIPS， ICML, Interspeech， ICASSP）和主要听力期刊（Trends in hearing， IJA, hearing research）上发表研究成果。进一步参考文献：Schlittenlacher， J, Baer， T.（2021）听力障碍的文本-语音转换，中文信息学报，2012.02174

Conversational artificial intelligence (AI), i.e. technology that a patient can talk to, has the potential to remove barriers from access to healthcare and in particular hearing healthcare. Imagine that people who worry about their hearing would not need to visit a GP or an audiologist for a first diagnosis but could just ask their home assistant "Please check my hearing". This could lead to an increasing awareness of hearing loss and uptake of hearing aids by the 1.5 billion people (2.5 billion in 2050) who suffer from a hearing loss. It is estimated that at least 6 million people in the UK would benefit from a hearing aid but only 2 million have one, and uptake among them is low and slow. A hearing test that takes place as a conversation with a technical device has further advantages to conventional technologies: First, it tests real-world sounds (speech) rather than the audibility of faint synthetic sounds (pure tones in an audiogram). Second, a conversational AI system can directly simulate the speech that would be delivered through a hearing aid and thus quantify the benefit that the best hearing-aid setting would give. Third, remote testing does not only remove barriers in accessing healthcare but is also of benefit to vulnerable patients. In this project we will apply text-to-speech (TTS) and automatic speech recognition (ASR) to hearing tests in order to achieve these goals. TTS and ASR will allow the patient to interact easily with the test system, which is an additional benefit for those who struggle with graphical user interfaces, in particular some elderly patients. The communication between patient and AI will be driven by the goals to have a more natural interaction than in conventional tests and to characterise a hearing loss in short time. The candidate should have an interest in healthcare and is expected to have a background in deep learning and other fields of artificial intelligence, obtained from a first degree in engineering, computer science, mathematics, physics or similar. The candidate will acquire skills in TTS, ASR and active learning. Collaborations with other young researchers and activities like journal clubs will contribute to establishing a basic knowledge in audiology and clinical testing. The candidate will produce research outputs that are published in machine-learning conferences (NeurIPS, ICML, Interspeech, ICASSP) and leading hearing journals (Trends in Hearing, IJA, Hearing Research). Further references: Schlittenlacher, J., Baer, T. (2021) Text-to-speech for the hearing impaired, arXiv, 2012.02174