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Aphasia is a type of acquired language impairment caused by brain injury. This paper presents an automatic speech recog- nition (ASR) based approach to objective assessment of apha- sia patients. A dedicated ASR system is developed to facilitate acoustical and linguistic analysis of Cantonese aphasia speech. The acoustic models and the language models are trained with domain- and style-matched speech data from unimpaired con- trol speakers. The speech recognition performance of this sys- tem is evaluated on natural oral discourses from patients with various types of aphasia.

Template based automatic segmentation of unit-database for TTS into phonetic and syllabic units.

Towards a better understanding of emotion in speech, it is important to understand how emotion changes and when it changes. Recognizing emotions using pre-segmented speech utterances results in a loss in continuity of emotions and does not provide insights into emotion changes. In this paper, we propose an investigation into emotion change detection from the perspective of exchangeability of data points observed sequentially using a martingale framework. Within the framework, a per-frame GMM likelihood based approach is proposed as a measure of strangeness from a particular emotion class.