Search

In this letter, we propose a layer selection method by permutations (LSMPs) of string select line (SSL) bias and string select transistor with multi-level states. Due to the increased number of threshold voltage orderings by the permutation, the number of required SSLs for the layer selection and the space occupied by SSLs can be minimized. Also, the operation scheme for the layer selection is discussed. To verify the operation of proposed LSMP, a fabricated pseudo-LSM is measured. As a result, it is clearly revealed that the number of selectable layer can be increased drastically by the LSMP. [ABSTRACT FROM PUBLISHER]

Words and natural language play a central role in how we describe and understand emotions. One can learn about emotions first-hand by observing physiological or behavioral data, but to communicate emotional information to others who are not first-hand observers, one must use natural language descriptions to communicate the emotional information. The field of affective computing deals with creating computer systems that can recognize and understand human emotions. To realize the goals of affective computing, it is necessary not only to recognize and model emotional behavior, but also to understand the language that is used to describe such emotional behavior. For example, a computer system that recognizes a user's emotion from speech should not only recognize the user's emotion from expressive speech acoustics, but also understand when the user says "I am beginning to feel X," where "X" is a variable representing some emotion word or description. The ability to understand descriptions of emotions is important not only for human-computer interaction, but also in deliberative decision making activities where deriving behavioral analytics is based on natural language (for example, in mental health assessments). Such analytics often rely on abstract scales that are defined in terms of natural language. [ABSTRACT FROM PUBLISHER]

This paper presents a novel student model intended to automate word-list-based reading assessments in a classroom setting, specifically for a student population that includes both native and nonnative speakers of English. As a Bayesian Network, the model is meant to conceive of student reading skills as a conscientious teacher would, incorporating cues based on expert knowledge of pronunciation variants and their cognitive or phonological sources, as well as prior knowledge of the student and the test itself. Alongside a hypothesized structure of conditional dependencies, we also propose an automatic method for refining the Bayes Net to eliminate unnecessary arcs. Reading assessment baselines that use strict pronunciation scoring alone (without other prior knowledge) achieve 0.7 correlation of their automatic scores with human assessments on the TBALL dataset. Our proposed structure significantly outperforms this baseline, and a simpler data-driven structure achieves 0.87 correlation through the use of novel features, surpassing the lower range of inter-annotator agreement. Scores estimated by this new model are also shown to exhibit the same biases along demographic lines as human listeners. Though used here for reading assessment, this model paradigm could be used in other pedagogical applications like foreign language instruction, or for inferring abstract cognitive states like categorical emotions. [ABSTRACT FROM PUBLISHER]

During expressive speech, the voice is enriched to convey not only the intended semantic message but also the emotional state of the speaker. The pitch contour is one of the important properties of speech that is affected by this emotional modulation. Although pitch features have been commonly used to recognize emotions, it is not clear what aspects of the pitch contour are the most emotionally salient. This paper presents an analysis of the statistics derived from the pitch contour. First, pitch features derived from emotional speech samples are compared with the ones derived from neutral speech, by using symmetric Kullback-Leibler distance. Then, the emotionally discriminative power of the pitch features is quantified by comparing nested logistic regression models. The results indicate that gross pitch contour statistics such as mean, maximum, minimum, and range are more emotionally prominent than features describing the pitch shape. Also, analyzing the pitch statistics at the utterance level is found to be more accurate and robust than "analyzing the pitch statistics for shorter speech regions (e.g., voiced segments). Finally, the best features are selected to build a binary emotion detection system for distinguishing between emotional versus neutral speech. A new two-step approach is proposed. In the first step, reference models for the pitch features are trained with neutral speech, and the input features are contrasted with the neutral model. In the second step, a fitness measure is used to assess whether the input speech is similar to, in the case of neutral speech, or different from, in the case of emotional speech, the reference models. The proposed approach is tested with four acted emotional databases spanning different emotional categories, recording settings, speakers and languages. The results show that the recognition accuracy of the system is over 77% just with the pitch features (baseline 50%). When compared to conventional classification schemes, the proposed approach performs better in terms of both accuracy and robustness. [ABSTRACT FROM AUTHOR]