Smarter Flexible Speech Recognition
Closer to the Human Brain Recognising Speech directly from the Audio
Smarter Flexible Speech Recognition
Closer to the Human Brain Recognising Speech directly from the Audio
Creating a More Flexible Speech Recognition
Born from decades of Peer-Reviewed Research
Professor Lahiri has held two ERC Advanced Grants in addition to two ERC Proof of Concept grants for work in this area, to carry out the fundamental investigation of variations in speech, leading to a linguistic model of speech based on phonological features, the articulatory and acoustic properties of each sound that form its contrasts with others. For example, the ‘voicing’ feature (whether the vocal cords are vibrating or not) forms a component of the contrast between the ‘p’ and ‘b’ consonant sounds in English.
Taken from Theory to Proof of Concept
Using the funding, the team developed the speech recognition system that was trained to recognise a universal set of 19 such features and can combine them to identify speech sounds, or phones. Importantly, it targets those features, essential to human understanding of speech, and ignores or tolerates those that can vary across speakers or utterances.
The research team has also used this model to develop a language learning app. which analyses words and sentences spoken by the user, and provides detailed feedback. Used in this way, language learners can receive personalised responses to improve their pronunciation.
Worldwide Patents Granted
The novel inventions for Automatic Speech Recognition and a System for Automatic Speech Analysis
INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT)
World Intellectual Property
Organization