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

对话式人工智能（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，特别是听力保健。想象一下，担心听力的人不需要去看全科医生或听力学家进行第一次诊断，而只需要问他们的家庭助理“请检查我的听力”。这可能会导致人们对听力损失的认识不断提高，并使15亿听力损失患者（2050年为25亿）使用助听器。据估计，英国至少有600万人将从助听器中受益，但只有200万人拥有助听器，而且他们的使用率很低而且很慢。与传统技术相比，以与技术设备对话的方式进行的听力测试具有更大的优势：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（听力图中的纯音）的可听度。其次，对话式人工智能系统可以直接模拟通过助听器传递的语音，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。在这个项目中，我们将应用文本到语音（TTS）和自动语音识别（ASR）的听力测试，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人来说是一个额外的好处，特别是一些老年患者。患者与人工智能之间的沟通将由目标驱动，以实现比传统测试更自然的互动，并在短时间内消除听力损失。候选人应该对医疗保健感兴趣，并希望拥有深度学习和其他人工智能领域的背景，从工程，计算机科学，数学，物理或类似的第一学位获得。候选人将获得TTS，ASR和主动学习的技能。与其他年轻研究人员的合作以及期刊俱乐部等活动将有助于建立听力学和临床测试的基础知识。候选人将产生在机器学习会议（NeurIPS，ICML，Interspeech，ICASSP）和领先的听力期刊（听力趋势，IJA，听力研究）上发表的研究成果。更多参考文献：Schlittenlacher，J.，Baer，T.（2021）听力障碍者的文本到语音，arXiv，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