Designing better hearing aids using physiologically inspired speech enhancement

助听器可以深刻地改变听力障碍者的生活，但仅在英国就有大约600万人不使用助听器。一个重要的原因是，传统的助听器不能很容易地区分有意义的信号，如语音和背景噪音。一个功能齐全的耳朵和大脑的组合在这项工作中表现得非常出色，即使只有少量的噪音，也可以轻松地超过最好的计算机语音识别应用程序。传统的助听器会不加区别地放大语音和噪音，因此即使大脑的神经通路可能没有受损，区分语音和噪音的任务也变得更加困难。自动语音增强的各种方法已经被尝试过，但没有一种接近自然的能力。尽管需要更好的解决方案，但最近的研究和开发进展缓慢，尚未出现突破性的技术。语音增强策略通常是基于数学而不是生理学原理开发的。这些方法虽然基于根本不同的策略，但有两个共同点：首先，它们主要依赖于信号的局部能量的信号特征，其次，它们没有提高语音清晰度。在这里，我们建议通过开发基于生理学原理的噪声中语音问题的工程解决方案来克服这一概念障碍。同样的技术也将有利于自动语音识别系统，因为两者的问题是相似的。我们希望在未来5年内看到我们的工作在助听器中的直接应用。世界上最大的两家公司（西门子助听器和谷歌信号处理）对这种方法表现出了兴趣，支持和信心。我们在整个开发周期中拥有丰富的经验：我们已经发明、设计、评估和实施了一种降噪方案，该方案将成为下一代Cobra Ltd.人工耳蜗的一部分。我们在这个建议中的中心假设是，大脑在区分有意义的信号和噪声时使用稀疏编码，并且它使用动态字典来表示声音。我们将研究听觉脑干中单个神经元的编码机制，并根据结果开发新的信号处理策略。我们期望这些算法将优于传统的算法，因此可以帮助听力受损。动物模型对这个项目至关重要，因为不可能研究人类听觉脑干中单个神经元的反应。稀疏神经元调整它们的反应，因为它们具有有限的动态速率，它们不断优化以响应环境，以减少冗余并最大化信息流。在这个项目中，我们将扩展静态神经反应模式的描述，以包括随时间变化和上下文敏感的组件，我们将测量这些动态响应在脑干中的单个神经元。了解神经元反应在噪声中的变化将使我们能够创建一个可用于稀疏化的动态字典。我们期望在这种表示中，语音和噪声是可分离的。我们将使用这些动态响应模式作为一种新的变换和稀疏化的基础，以提高语音的理解相关的组件，从而提高语音清晰度，而不会降低质量。我们将在大量的临床试验中评估该算法。

Hearing aids can profoundly transform the lives of people with hearing impairments but in the UK alone about 6 million such people do not use them. An important reason for this is that conventional hearing aids don't make it easy to distinguish meaningful signals such as speech from background noise. The combination of a fully functioning ear and the brain are fantastically good at this job and easily outperform the best computerized speech-recognition apps when even just a small amount of noise is present. A conventional hearing aid amplifies both the speech and noise indiscriminately, so even though the neural pathways of the brain may be unimpaired the task of distinguishing speech from noise becomes much harder. Various approaches to automatic speech enhancement have been tried but none comes close to what nature can do.Despite the demand for better solutions, recent progress in research and development has been slow and no breakthrough technology has yet emerged. Speech enhancement strategies have been generally developed on the basis of mathematical, but not physiological principles. These methods, although based on fundamentally different strategies, have two things in common: first, they operate on signal features that rely primarily on the signals' local energy, and second, they have not improved speech intelligibility. Here, we propose overcoming this conceptual barrier by developing engineering solutions to the speech-in-noise problem that are based on physiological principles. The same technology will also be of benefit for automatic speech recognition systems, since the problems of both are similar.We expect to see direct applications of our work to be implemented in hearing aids within the next 5 years. Two of the biggest companies in the world in their field (Siemens for hearing aids and Google for signal processing) demonstrate the interest, support and the confidence toward this approach. We have substantial experience with the whole development cycle: we have invented, designed, evaluated and implemented a noise reduction scheme that will be part of the next generation of Cochlea Ltd. cochlear implants. Our central hypothesis in this proposal is that the brain uses sparse coding when distinguishing meaningful signals from noise and it uses a dynamic dictionary for sound representation. We are going to investigate this coding mechanism in individual neurons in the auditory brainstem, and based on the results, will develop novel signal-processing strategies. We expect that these algorithms will be better than conventional algorithms and consequently can help hearing impaired. An animal model is essential to this project, because it is impossible to study responses of individual neurons from the auditory brainstem in humans. Sparse Neuron adapt their response because they have a limited dynamic rate which they constantly optimize in response to the environment in order to reduce redundancy and to maximise the information flow. In this project, we are going to extend the description of static neural response patterns to include a time varying and context sensitive components and we will measure these dynamic responses in single neurons in the brain stem. Knowing how neuronal responses change in noise will enable us to create a dynamic dictionary that can be used for sparsification. We expect that in this representation speech and noise is separable. We will use these dynamic response pattern as the basis of a novel transformation and sparsification in order to enhance the components of speech that are relevant for understanding, thus improving speech intelligibility without reducing the quality. We will evaluate the algorithm in substantial clinical trials.