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Isolated Digit Recognition Without Time Alignment, Jeffrey M. Gay
Isolated Digit Recognition Without Time Alignment, Jeffrey M. Gay
Theses and Dissertations
This thesis examines methods for isolated digit recognition without using time alignment. Resource requirements for isolated word recognizers that use time alignment can become prohibitively large as the vocabulary to be classified grows. Thus, methods capable of achieving recognition rates comparable to those obtained with current methods using these techniques are needed. The goals of this research are to find feature sets for speech recognition that perform well without using time alignment, and to identify classifiers that provide good performance with these features. Using the digits from the TI46 database, baseline speaker-independent recognition rates of 95.2% for the complete speaker …
Multiclassifier Fusion Of An Ultrasonic Lip Reader In Automatic Speech Recognition, David L. Jennnings
Multiclassifier Fusion Of An Ultrasonic Lip Reader In Automatic Speech Recognition, David L. Jennnings
Theses and Dissertations
This thesis investigates the use of two active ultrasonic devices in collecting lip information for performing and enhancing automatic speech recognition. The two devices explored are called the 'Ultrasonic Mike' and the 'Lip Lock Loop.' The devices are tested in a speaker dependent isolated word recognition task with a vocabulary consisting of the spoken digits from zero to nine. Two automatic lip readers are designed and tested based on the output of the ultrasonic devices. The automatic lip readers use template matching and dynamic time warping to determine the best candidate for a given test utterance. The automatic lip readers …
Clustering Techniques In Speaker Recognition, Douglas N. Prescott
Clustering Techniques In Speaker Recognition, Douglas N. Prescott
Theses and Dissertations
This thesis presents a comparison based on identification rate, of three clustering techniques applied to cepstral features for speaker identification. LBG vector quantization as developed by Linde, Buzo and Gray; is used to provide benchmark performance for comparison with Fuzzy clustering (based on the unsupervised fuzzy partition-optimal number of classes, UFP-ONC algorithm by Gath and Geva) and an Artificial Neural Network, the Multilayer Perceptron. Cepstral features from the TIMIT, King and AFIT93 corpus speaker databases are used to produce speaker-identification classifiers using each of the clustering algorithms. The experiment reported evaluates the speaker identification performance using the 20-dimensional cepstral features …