Persistent hiccup in a continuous ambulatory peritoneal dialysis patient following ingestion of star fruit

AbstractAnimal studies have suggested that ingestion of star fruit, which belongs to the Oxalidaceae family, may cause neurotoxicity. Outbreaks of intractable hiccups have been reported following ingestion of star fruit in patients on regular hemodialysis. Other complaints have included insomnia, agitation and mental confusion. We report a patient on continuous ambulatory peritoneal dialysis (CAPD) who presented with acute confusion and persistent hiccup following ingestion of star fruit. Symptoms resolved spontaneously. Other reported cases and management strategies are discussed

PLASER: Pronunciation Learning via Automatic Speech Recognition

PLASER is a multimedia tool with instant feedback designed to teach English pronunciation for high-school students of Hong Kong whose mother tongue is Cantonese Chinese. The objective is to teach correct pronunciation and not to assess a student's overall pronunciation quality. Major challenges related to speech recognition technology include: allowance for non-native accent, reliable and corrective feedbacks, and visualization of errors

Speaker Verification Using Adapted Articulatory Featurebased Conditional Pronunciation Modeling

This paper proposes a speaker verification system based on articulatory feature-based conditional pronunciation modeling (AFCPM). The system captures the pronunciation characteristics of speakers by modeling the linkage between the actual phones produced by the speakers and the state of articulations during speech production. The speaker models, which consist of conditional probabilities of two articulatory classes, are adapted from a set of universal background models (UBMs) via MAP adaptation. This creates a direct coupling between the speaker and background models, which prevents over-fitting the speaker models when the amount of speaker data is limited. Experimental results demonstrate that MAP adaptation not only enhances the discriminative power of the speaker models but also improves their robustness against handset mismatches. Results also show that fusing the scores derived from an AFCPM-based system and a conventional spectral-based system achieves an error rate that is significantly lower than that can be achieved by the individual systems. This suggests that AFCPM and spectral features are complementary to each other. 1

ADAPTIVE CONDITIONAL PRONUNCIATION MODELING USING ARTICULATORY FEATURES FOR SPEAKER VERIFICATION

This paper proposes an articulatory feature-based conditional pronunciation modeling (AFCPM) technique for speaker verification. The technique models the pronunciation behaviors of speakers by creating a link between the actual phones produced by the speakers and the state of articulations during speech production. Speaker models consisting of conditional probabilities of two articulatory classes are adapted from a set of universal background models (UBMs) using MAP adaptation technique. This adaptation approach aims to prevent over-fitting the speaker models when the amount of speaker data is insufficient for a direct estimation. Experimental results show that the adaptation technique can enhance the discriminating power of speaker models by establishing a tighter coupling between speaker models and the UBMs. Results also show that fusing the scores derived from an AFCPM-based system and a conventional spectral-based system achieves a significantly lower error rate than that of the individual systems. This suggests that AFCPM and spectral features are complementary to each other. 1