| The de-facto standard for dysarthric intelligibility assessment is a subjective intelligibility test, performed by an expert. Subjective tests are often costly, biased and inconsistent because of their perceptual nature. Automatic objective assessment methods, in contrast, are repeatable and relatively cheap. Objective methods can be broken down into two subcategories: reference-free, and reference based. Reference-free methods employ estimation procedures that do not require information about the target speech material. This potentially makes the problem more difficult, and consequently, there is a deficit of research into reference-free dysarthric intelligibility estimation.;In order to gauge the complimentariness of P.563 and proposed features, a linear intelligibility model is proposed and tested. Intelligibility is expressed as a linear combination of acoustic features, which are selected from a feature pool using speaker-dependent and speaker-independent validation methods. An intelligibility estimator constructed with only P.563 features serves as the 'baseline'. When proposed features are added to the feature pool, performance is shown to improve substantially for both speaker-dependent and speaker-independent methods when compared to the baseline. Results are also shown to compare favourably with those reported in the literature.;In this thesis, we focus on the reference-free intelligibility estimation approach. To make the problem more tractable, we focus on the dysarthrias of cerebral palsy (CP). First, a popular standard for blind speech quality estimation, the ITU-T P.563 standard, is examined for possible application to dysarthric intelligibility estimation. The internal structure of the standard is discussed, along with the relevance of its internal features to intelligibility estimation. Afterwards, several novel features expected to relate to some of the acoustic properties of dysarthric speech are proposed. Proposed features are based on the high-order statistics of parameters derived from linear prediction (LP) analysis, and a mel-frequency filterbank. |
