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Abstract A Keyword Spotting System (KWS) is a system that recognizes predefined keywords in spoken utterances or written documents. The objective is to obtain the highest possible keyword detection rate without increasing the number of false detections in a system. The common approach to keyword spotting is the use of a Hidden Markov Model (HMM). These are usually complex systems which require training speech data. The Typical HMM approach uses garbage templates or HMM models to match non-keyword speech and non-speech sounds. The purpose of this research is to design a simple Keyword Spotting System. The system will be designed to spot English words and should be easily adaptable to other languages There are many challenges in designing a keyword spotting system such as variations in speech like pitch, loudness, timbre that make recognition difficult. There can be wide variations in utterances even from the same speaker. In this research, the use of cross-correlation, as an alternative means for detecting keywords in an utterance, was investigated. This research also involves the modeling of a global keyword using a quantized dynamic time warping algorithm, which can function effectively with multi-speakers. The global keyword is an aggregation of the features from several occurrences of the same keyword. This research also investigates the effect of pitch normalization on keyword detection. The use of cross-correlation as a method for keyword spotting was investigated in both the time and MFCC domain. In the time domain the global keyword was cross-correlated with a pitch-normalized utterance. A zero lag ratio (the ratio of the power around the zero lag obtained from a cross correlation to the power in the rest of the signal is computed) was computed for each speech frame, a threshold was then used to determine if the keyword is present. For the MFCC domain the MFCC features of each keyword were computed, normalized and cross-correlated with the normalized MFCC features of portions of the utterance of the same size as the keyword. Cross-correlation of MFCC features of the keyword with that of each portion of the utterance yields a single value between 0-1. The portion with the highest value is usually the location of the keyword. Results in the time domain varied from keyword to keyword, some words showed a 60% hit rate while the average obtained from various keywords from the Call Home database had an average of 41%. Cross-correlation of the keywords and utterance in the MFCC domain yielded a 66% hit rate in test conducted on all different keywords in the Call Home and Switchboard corpus. The system accuracy is keyword dependent with some keywords having an 85% hit rate / Electrical and Computer Engineering

Matching relations identified by Herrnstein (1961) demonstrate behavior rates as a function of the ratio of reinforcement rates for alternative behaviors. The mathematical relationship identified by Herrnstein and modified by Baum (1974) was later applied to foraging animals, given the option of two patches (Kennedy & Gray, 1993). This application lead to the demonstration that animals (Baum & Kraft, 1998) and humans (Kraft & Baum, 2001) will engage in emergent group behavior that is distinct from individual matching relations. To more completely understand group matching in humans, group relations beyond foraging must be investigated. This study expands on the findings of Kraft and Baum (2001) and subsequent studies by introducing a group contingency for point earnings. Using an ABAB/BABA experimental design, interdependent group contingency was compared to individual contingency in two experimental sessions with different sets of participants. To better understand individual performance in matching rations, a temporal discounting measure was administered to participants (Beck & Triplett, 2009). Scores were transformed into area-under-the-curve values and correlated with total points earned. Participants were divided into teams based on their scores on a measure of temporal discounting with which they earned points during the group contingency conditions. An effect for group contingency was found for Experiment 2 but not Experiment 1. Order effects apparent in the data from both experiments are attributed to the BABA design used in Experiment 2. Results across the two experiments show a relationship between temporal discounting scores and total points earned for participants with valid temporal discounting scores (n = 13). Future research should expand upon these findings in applied contexts. / Educational Psychology

In January of 2012, Philadelphia Mayor Michael Nutter outlined the crime fighting measures that his administration would pursue during his second term as mayor. Included was a plan to introduce a multi-agency crime reduction program, which Philadelphia Police Commissioner Charles Ramsey and District Attorney Seth Williams would co-chair, called GunStat. GunStat was described as a collaborative effort to reduce gun violence through (1) identifying locations with a high incidence of violent crime, (2) pinpointing violent offenders responsible for these crimes, (3) focusing on arresting and prosecuting these offenders for crimes committed at these places, and (4) enhanced monitoring of offenders on probation and parole who are living and/or offending within these locations. In effect, GunStat was designed to target the right people (prolific, violent known offenders) at the right places (hot spots of violent crime). This dissertation is an in-depth, ex post facto evaluation of Philadelphia’s GunStat model as implemented over two phases and two years. It involved both a quasi-experimental research design which employed propensity score matching methods to generate comparisons, and a process-evaluation where several themes, including program implementation, were explored. The results here suggest that GunStat did not reduce crime relative to comparison locations. However, the qualitative data highlighted the importance of informal inter-agency networks that were developed during the course of the intervention, and suggested that GunStat put future collaborations on a solid footing. The implications for criminal justice policy, theory and evaluation design are discussed. / Criminal Justice