Visualizing confusion matrices for multidimensional signal detection correlational methods
نویسندگان
چکیده
Advances in modeling and simulation for General Recognition Theory have produced more data than can be easily visualized using traditional techniques. In this area of psychological modeling, domain experts are struggling to find effective ways to compare large-scale simulation results. This paper describes methods that adapt the web-based D3 visualization framework combined with pre-processing tools to enable domain specialists to more easily interpret their data. The D3 framework utilizes Javascript and scalable vector graphics (SVG) to generate visualizations that can run readily within the web browser for domain specialists. Parallel coordinate plots and heat maps were developed for identification-confusion matrix data, and the results were shown to a GRT expert for an informal evaluation of their utility. There is a clear benefit to model interpretation from these visualizations when researchers need to interpret larger amounts of simulated data.
منابع مشابه
Visualizing Confusion Matrices for Multidimensional Signal Detection Correlational Methods and Semantic Cluster Based Visualization in Virtual Environments
متن کامل
Analysis of inter-transcriber consistency in the Cat_ToBI prosodic labeling system
A set of tools to analyze inconsistencies observed in a Cat_ToBI labeling experiment are presented. We formalize and use the metrics that are commonly used in inconsistency tests. The metrics are systematically applied to analyze the robustness of every symbol and every pair of transcribers. The results reveal agreement rates for this study that are comparable to previous ToBI inter-reliability...
متن کاملSpeech, Hearing and Language: work in progress Volume 14 A CHOICE THEORY METHOD FOR EVALUATING AUDIOVISUAL PHONEME RECOGNITION
This article describes a mathematical method, based on Choice Theory (e.g., Luce, 1963), that can be used to predict audiovisual phoneme confusion matrices from unimodal audio and visual data. The predictions made from this method can be compared to obtained levels of audiovisual processing, for the purpose of identifying individuals whose audiovisual integration processes are not efficient. A ...
متن کاملEnhancing Data-Driven Phone Confusions Using Restricted Recognition
This paper presents a novel approach to address data sparseness in standard confusion matrices and demonstrates how enhanced matrices, which capture additional similarities, can impact the performance of spoken term detection. Using the same training data as for the standard phone confusion matrix, an enhanced confusion matrix is created by iteratively restricting the recognition process to exc...
متن کاملDetection of point landmarks in multidimensional tensor data
This paper describes a unified approach to the detection of point landmarks-whose neighborhoods convey discriminant information-including multidimensional scalar, vector, and higher-order tensor data. The method is based on the interpretation of generalized correlation matrices derived from the gradient of tensor functions, a probabilistic interpretation of point landmarks, and the application ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2014