A Novel Method For Speech Segmentation Based On Speakers' Characteristics

Speech Segmentation is the process change point detection for partitioning an input audio stream into regions each of which corresponds to only one audio source or one speaker. One application of this system is in Speaker Diarization systems. There are several methods for speaker segmentation; however, most of the Speaker Diarization Systems use BIC-based Segmentation methods. The main goal of this paper is to propose a new method for speaker segmentation with higher speed than the current methods - e.g. BIC - and acceptable accuracy. Our proposed method is based on the pitch frequency of the speech. The accuracy of this method is similar to the accuracy of common speaker segmentation methods. However, its computation cost is much less than theirs. We show that our method is about 2.4 times faster than the BIC-based method, while the average accuracy of pitch-based method is slightly higher than that of the BIC-based method.Comment: 14 pages, 8 figure

Floquet engineering in superconducting circuits: from arbitrary spin-spin interactions to the Kitaev honeycomb model

We derive a theory for the generation of arbitrary spin-spin interactions in superconducting circuits via periodic time modulation of the individual qubits or the qubit-qubit interactions. The modulation frequencies in our approach are in the microwave or radio frequency regime so that the required fields can be generated with standard generators. Among others, our approach is suitable for generating spin lattices that exhibit quantum spin liquid behavior such as Kitaev's honeycomb model.Comment: 21 pages, 9 figure

Influence of in-store and out-of-store creative advertising strategies on consumer attitude and purchase intention

Purpose: With regard to the fact that people usually try to avoid repetitive and boring advertisements, creativity as the heart of advertising effectiveness has a significant role in drawing their attention. On this basis, the present study attempts to evaluate the influence of creative advertising strategies by comparing “in-store” and “out-of-store” creative advertisements. Design/methodology: This research has been conducted in Tehran (capital of Iran) and 588 volunteers randomly participated in the survey so as to examine the consumers’ attitude/behaviour towards the advertised brand, advertised product, and purchases intention exposing creative in-store and out-of-store advertisements. In the current study, creative “endof- aisle display stands” in grocery stores/supermarkets represent in-store advertising media, and creative “TV commercials” represent out-of-store advertising. Furthermore to examine the hypotheses, one-sample t-test and paired sample t-test were used. Findings: The results show that creative out-of-store advertising has influence primarily on attitude towards the advertised brand, then on attitude towards the advertised product, and finally on the purchases intention. On the other hand, creative in-store advertising, firstly has influence on the purchase intention, then on attitude towards the advertised brand, and lastly on attitude towards the advertised product. The findings provide important insights to the formulation of strategic marketing/advertising and would pave the related innovative ways to capitalize on strategic opportunities. Originality/value: The study is the first survey comparing the effectiveness of in-store and out-of-store creative advertising in order to find out a strategic marketing/advertising solution.Peer Reviewe

Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition

Automatic speech recognition performance degrades significantly when speech is affected by environmental noise. Nowadays, the major challenge is to achieve good robustness in adverse noisy conditions so that automatic speech recognizers can be used in real situations. Spectral subtraction (SS) is a well-known and effective approach; it was originally designed for improving the quality of speech signal judged by human listeners. SS techniques usually improve the quality and intelligibility of speech signal while speech recognition systems need compensation techniques to reduce mismatch between noisy speech features and clean trained acoustic model. Nevertheless, correlation can be expected between speech quality improvement and the increase in recognition accuracy. This paper proposes a novel approach for solving this problem by considering SS and the speech recognizer not as two independent entities cascaded together, but rather as two interconnected components of a single system, sharing the common goal of improved speech recognition accuracy. This will incorporate important information of the statistical models of the recognition engine as a feedback for tuning SS parameters. By using this architecture, we overcome the drawbacks of previously proposed methods and achieve better recognition accuracy. Experimental evaluations show that the proposed method can achieve significant improvement of recognition rates across a wide range of signal to noise ratios

Persian Keyphrase Generation Using Sequence-to-Sequence Models

Keyphrases are a very short summary of an input text and provide the main subjects discussed in the text. Keyphrase extraction is a useful upstream task and can be used in various natural language processing problems, for example, text summarization and information retrieval, to name a few. However, not all the keyphrases are explicitly mentioned in the body of the text. In real-world examples there are always some topics that are discussed implicitly. Extracting such keyphrases requires a generative approach, which is adopted here. In this paper, we try to tackle the problem of keyphrase generation and extraction from news articles using deep sequence-to-sequence models. These models significantly outperform the conventional methods such as Topic Rank, KPMiner, and KEA in the task of keyphrase extraction

Derived thermodynamic properties of [o-xylene or p-xylene + (acetic acid or tetrahydro-furan)] at different temperatures and pressures

Thermal expansion coefficients α, their excess values , isothermal coefficient of pressure excess molar enthalpy , partial molar volumes and excess partial molar volumes , were calculated from experimental densities. The isothermal coefficients of pressure excess molar enthalpy for binary mixtures {o-xylene or p-xylene + acetic acid} at temperatures 313.15-473.15 K and pressure 0.2-2 MPa are negative and for binary mixtures {o-xylene or p-xylene + tetrahydrofuran (THF)} at temperatures 278. 15 K to 318.15 K and pressure 81.5 kPa are negative and with increasing temperature become more negative. The excess thermal expansions coefficient , for binary mixtures {o-xylene or p-xylene + acetic acid} at temperatures 313.15-473.15 K and pressure 0.2 MPa and 2 MPa are positive. The excess thermal expansions coefficient for binary mixtures {o-xylene or p-xylene + tetrahydrofuran (THF)} at temperatures 278.15-318.15 K and pressure 81.5 kPa are positive and with increasing temperature become more positive. The excess molar volumes were correlated with a Redlich–Kister type equation.KEY WORDS: Thermal expansion coefficients, Isothermal coefficient, Excess partial molar volumes Bull. Chem. Soc. Ethiop. 2011, 25(2), 273-286.

Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification

This paper presents the models submitted by Ghmerti team for subtasks A and B of the OffensEval shared task at SemEval 2019. OffensEval addresses the problem of identifying and categorizing offensive language in social media in three subtasks; whether or not a content is offensive (subtask A), whether it is targeted (subtask B) towards an individual, a group, or other entities (subtask C). The proposed approach includes character-level Convolutional Neural Network, word-level Recurrent Neural Network, and some preprocessing. The performance achieved by the proposed model for subtask A is 77.93% macro-averaged F1-score