Browsing by Subject "Automatic speech recognition."
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Open Access Analysis of a hospital call center(Bilkent University, 2012) Budak, Ezel EzgiIn this thesis, we study the call center operations of a particular hospital located in Ankara, namely Güven Hospital. The hospital call center takes role as a medical consulting and appointment center and also domestic call traffic flows over the call center. These three types of calls are classified as consulting, appointment and domestic calls. The arriving call rate to the call center vary depending on hours and each agent is capable of giving service to each type of calls.( i.e. Agents are multitasking). Different types of calls have different exponential service time distributions. Regardless of call type calls may abandon during their waiting time in the call center. Abandonment rate and arrival rates of the calls are assumed to be exponential. Call center directs some percent of appointment calls to a doctor who gives service in the call center and some percent of consulting calls to hospital units depending on customers’ requests. A domestic call only receives service from the call center. Some percent of these diverted calls to doctor and hospital units return to call center. This diverting and returning process among call center, doctor and hospital units constitutes the call center network of the hospital. The aim of this study is to model this call center network as to reflect the properties of the analyzed system. Accordingly, the system is modeled with queuing network and simulation approaches. Different models are developed with different divert and return rates and different number of agents being multi-tasking or dedicated to give service to a specific call type. These models are compared in terms of systems performance metrics and related numerical analyses are reported.Item Open Access Automatic speech segmentation based on subband decomposition(Bilkent University, 1999) Bozkurt, ArcinItem Open Access Large vocabulary speech recognition in noisy environments(Bilkent University, 1998) Jabloun, FirasA ІКПѴ set of speech feature parameters based on multirate subband analysis and the Teager Energy Operator (TEO) is developed. The speech signal is first divided into nonuniform subbands in mel-scale using a multirate filter-bank, then the Teager energies of the subsignals are estimated. Finally, the feature vector is constructed by logcompression and inverse DOT computation. The new feature parameters (TEOCEP) have a robust speech recognition performance in car engine noise which has a low pass nature. In this thesis, we also present some solutions to the problem of large vocabulary speech recognition. Triphone-based Hidden Markov. Models (HMM) are used to model the vocabulary words. Although the straight forward parallel search strategy gives good recognition performance, the processing time required is found to be long and impractical. Therefore another search strategy with similar performance is described. Subvocabularies are developed during the training session to reduce the total number of words considered in the search process. The search is then performed in a tree structure by investigating one subvocabulary instead of all the words.