Audio-Visual Speech Enhancement and Speaker Separation

The problem with audio perception is that individual sounds are mixed together with unknown acoustic reverberations, and this makes it impossible to extract them without prior knowledge of the source characteristics. The problem of audio-source separation is a fundamental problem in audio perception.
Humans have the ability to understanding speech when it is mixed with other types of sound and noise; by isolating and focusing attention to one voice from a multitude. This research aims to reproduce or model this accomplishment of the brain with computational and algorithmic means.

Speech enhancement is a method of increasing speech intelligibility by using algorithms to separate and enhance the original source of the speech from others. Automating the process of speech enhancement has many real-world applications such as increasing the effectiveness of assistive technology for the hearing impaired, creating virtual reality with high clarity and better transcription of speech in noisy audio tracks. Additionally, with ever-increasing use of audio-visual and voice-controlled technologies, the ability to capture and enhance a speaker's voice is becoming imperative in the robustness of automatic speech recognition (ASR) systems. These systems tend to infer speech well in quiet environments, but they struggle when background noise is present.

Although recently there has been significant advancement in speech separation using deep learning methods, it is still considered a difficult problem due to time-variant input signals and high variability of reverberant sound fields. Traditionally the task of speech enhancement is either performed on audio-only tracks or the combination of audio and video inputs. Deep learning techniques have been applied to challenging tasks such as removing background noise from speech, separating a speaker from multiple speech signals, or more generally separating arbitrary classes of sound from each other.

This work will address the shortcomings of the current methods and will explore conditioning speech separation tasks by conditioning on complementary information, such as visual cues from the speaker's lip motions.

AIMS's impact will be felt across domains of acute need within the UK. We expect AIMS to benefit: UK economic performance, through start-up creation; existing UK firms, both through research and addressing skills needs; UK health, by contributing to cancer research, and quality of life, through the delivery of autonomous vehicles; UK public understanding of and policy related to the transformational societal change engendered by autonomous systems.

Autonomous systems are acknowledged by essentially all stakeholders as important to the future UK economy. PwC claim that there is a £232 billion opportunity offered by AI to the UK economy by 2030 (10% of GDP). AIMS has an excellent track record of leadership in spinout creation, and will continue to foster the commercial projects of its students, through the provision of training in IP, licensing and entrepreneurship. With the help of Oxford Science Innovation (investment fund) and Oxford University Innovation (technology transfer office), student projects will be evaluated for commercial potential.

AIMS will also concretely contribute to UK economic competitiveness by meeting the UK's needs for experts in autonomous systems. To meet this need, AIMS will train cohorts with advanced skills that span the breadth of AI, machine learning, robotics, verification and sensor systems. The relevance of the training to the needs of industry will be ensured by the industrial partnerships at the heart of AIMS. These partnerships will also ensure that AIMS will produce research that directly targets UK industrial needs. Our partners span a wide range of UK sectors, including energy, transport, infrastructure, factory automation, finance, health, space and other extreme environments.

The autonomous systems that AIMS will enable also offer the prospect of epochal change in the UK's quality of life and health. As put by former Digital Secretary Matt Hancock, "whether it's improving travel, making banking easier or helping people live longer, AI is already revolutionising our economy and our society." AIMS will help to realise this potential through its delivery of trained experts and targeted research. In particular, two of the four Grand Challenge missions in the UK Industrial Strategy highlight the positive societal impact underpinned by autonomous systems. The "Artificial Intelligence and data" challenge has as its mission to "Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030". To this mission, AIMS will contribute the outputs of its research pillar on cancer research. The "Future of mobility" challenge highlights the importance the autonomous vehicles will have in making transport "safer, cleaner and better connected." To this challenge, AIMS offers the world-leading research of its robotic systems research pillar.

AIMS will further promote the positive realisation of autonomous technologies through direct influence on policy. The world-leading academics amongst AIMS's supervisory pool are well-connected to policy formation e.g. Prof Osborne serving as a Commissioner on the Independent Commission on the Future of Work. Further, Dr Dan Mawson, Head of the Economy Unit; Economy and Strategic Analysis Team at BEIS will serve as an advisor to AIMS, ensuring bidirectional influence between policy objectives and AIMS research and training.

Broad understanding of autonomous systems is crucial in making a society robust to the transformations they will engender. AIMS will foster such understanding through its provision of opportunities for AIMS students to directly engage with the public. Given the broad societal importance of getting autonomous systems right, AIMS will deliver core training on the ethical, governance, economic and societal implications of autonomous systems.