Creation of a speech synthesis model from spontaneous speech

Keywords: Machine learning, speech synthesis, low resource languages, Nigerian Pidgin

Objectives The main aim is to produce a natural-sounding text-to-speech (TTS) model allowing to perform perceptual tests for experimental linguistics. Thanks partly to the recent evolution of neural network-based speech technologies, researchers can now produce high-quality synthesis from relatively simple datasets using models like TacoTron 2, complementing classical approaches such as those based on Hidden Markov Models. Specifically, the intern will assist in developing a text-to-speech platform trained on an existing database of Nigerian Pidgin recordings. In addition to producing natural-sounding speech, a central goal of this project will be to build a TTS model that will allow for the direct modification of intonational patterns via explicit parameters provided by researchers. The intern’s work will contribute to the exploration of the language’s melodic and tonal properties by allowing researchers to produce variations of novel utterances differing only by their intonational patterns. Context This work is part of a larger project to study Nigerian Pidgin. It is a large but under-resourced language that increasingly serves as the primary vernacular language of Africa’s most populous country. Once stigmatized as a “broken” variety of English spoken only by the uneducated, Nigerian Pidgin is now a source of pride for many speakers who view it as a home-grown vehicle for communication. It transcends class and ethnicity, lacking the tribal associations of indigenous languages and the colonial baggage associated with English. The language can now be seen and heard in college campuses, houses of worship, advertisements, Nigerian expat communities, and even on a local branch of the BBC.

Primary tasks

• Surveying existing TTS models and selecting the most suitable approach

• Training a model on a corpus of Nigerian Pidgin

• Optimizing and evaluating the model Profile

A second-year master’s student with:

• A solid background in machine learning (speech synthesis is a plus)

• Good academic writing skills in English • An strong interest in language and linguistics