This paper presents a novel way to speed up the classification time of a boosting classifier. We make the shallow (flat) network deep (hierarchical) by growing a tree from the decision regions of a given boosting classifier. This provides many short paths for speeding up and preserves the Boosting decision regions, which are reasonably smooth for good generalisation. We express the conversion a...

Recently, a rapid growth in the population in urban regions demands the provision of services and infrastructure. These needs can be come up wit the use of Internet of Things (IoT) devices, such as sensors, actuators, smartphones and smart systems. This leans to building Smart City towards the next generation Super City planning. However, as thousands of IoT devices are interconnecting and comm...

In this paper we describe the techniques and methodology developed for automatic labeling of segmental and prosodic information for the Mandarin speech database. There are two major procedures. First, the text is converted into the phonetic network of possible pronunciations, and this network is aligned with the speech data by recognition processes. Secondly, many acoustic prosodic features are...

In this work, we quantify common tonal and phonetic errors made by second language learners of Mandarin Chinese. Pronunciation patterns of 300 native speakers of European languages are analyzed. Tonal errors (30.56%) are found to be more prevalent than phonetic ones (8.71%). Common errors include overemphasis of Tone 3 and inadequate aspiration of affricate consonants. Decision tree clustering ...

We describe preliminary work on the detection of word fragments in Mandarin conversational telephone speech. We extracted prosodic, voice quality, and lexical features, and trained Decision Tree and SVM classifiers. Previous research shows that glottalization features are instrumental in English fragment detection. However, we show that Mandarin fragments are quite different than English; 90% o...

In this paper we present the performance analysis of different data mining techniques to predict the inpatient hospital length of stay in a super specialty hospital. Data set used for the analysis is real time data taken from super specialty hospital. Pre-processed data set is generated from the electronic discharge summaries obtained from the hospital. This data set consists of 401 records wit...