Synthetic voices are becoming ubiquitous: `smart' speakers at home, announcement systems on public transport, and voice-enabled assistants on call lines. There exist a strong public demand for `smarter' assistants capable of laughing at our jokes; interacting with our children as encouraging and emphatic tutors; calling to check up on our parents; providing a reassuring `ear' for an isolated person; and offering calming and supportive virtual therapy. To support current and future applications, voice synthesis technology needs to satisfy a number of requirements. First, it needs to be customisable for rapid research and development, and second, it needs to be able to produce any spoken content, including expressive voice characteristics. However, none of the current synthesis technologies can simultaneously satisfy all of the above requirements. For instance, while current non-machine learning approaches allow pre-recorded phrases to be efficiently combined into complete sentences, it also means that missing necessary phrases must be recorded first, thereby limiting their flexibility and efficiency. On the other hand, current machine learning models can seamlessly synthesise any spoken content. However, creating such models is a very costly, time-consuming and computationally demanding process. Furthermore, these models offer a very limited control over the qualities of the voice characteristics and lack interpretability, which are highly desirable conditions in both research and commercial settings.
In this project, the objective is to develop a computationally efficient, customisable, expressive and interpretable speech synthesis, by drawing from the concept of `exemplars' in cognitive science.
In the field of cognitive science, the notions of `exemplars' and `prototypes' form a part of a prominent view on how humans categorise concepts. In particular, exemplar theory argues that singular examples, rather than prototypes (an average of examples), form the basic building blocks of how we understand and interact with the world. The key argument in favour of exemplar theory is our ability as humans to solve complex tasks based on just a few examples, which makes this theory appealing to applications that involve complex phenomena or that require high computational efficiency. Furthermore, expressive speech synthesis combines expressivity and speech production, which are two complex phenomena that remain poorly understood. Unlike prototype theory, exemplar theory, at least theoretically, enables to produce expressive speech, provided that at least one recording of the desired spoken content and one recording featuring the desired expressivity are available. Lastly, adopting exemplar theory promotes transparency during the decision making process through the use of real examples that can be inspected, modified, replaced, added, etc. within the task.
The objective will be achieved through three innovative means by: i) formulating a methodological framework for exemplar-based speech synthesis, ii) building an exemplar-based representation for speech expressivity from pre-recorded examples and iii) presenting a novel methodology for integrating this expressivity-based representation into the framework of i).
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