Detecting language structure in audio signals

Having received a signal, unlike traditional speech processing, the aim of this research goal is not to identify where individual word boundaries begin and end or detect the pattern set, using supervised techniques, which comprise the signal s lexicon. The rationale that underpins this approach is therefore, not to decipher the audio signal content as this is a secondary task and assumes language content exists, but to identify what constitutes the physical structure of spoken language, in contrast to other structured phenomena. In essence, to develop an automated (artificially intelligent) intuitive ear that can detect the rhythm and structure of language with the same accuracy (or better) of the human ear. To achieve this, unsupervised learning techniques, which do not rely on prior knowledge of a specific system, underpin generic methods devised, to facilitate classification of unknown phenomena, if encountered.