Abstract

Contemporary datasets on tobacco consumption focus on one of two topics, either public health mentions and disease surveillance, or sentiment analysis on topical tobacco products and services. However, two primary considerations are not accounted for, the language of the demographic affected and a combination of the topics mentioned above in a finegrained classification mechanism. In this paper, we create a dataset of 3144 tweets, which are selected based on the presence of colloquial slang related to smoking and analyze it based on the semantics of the tweet. Each class is created and annotated based on the content of the tweets such that further hierarchical methods can be easily applied. Further, we prove the efficacy of standard text classification methods on this dataset, by designing experiments which do both binary as well as multi-class classification. Our experiments tackle the identification of either a specific topic (such as tobacco product promotion), a general mention (cigarettes and related products) or a more fine-grained classification.This methodology paves the way for further analysis, such as understanding sentiment or style, which makes this dataset a vital contribution to both disease surveillance and tobacco use research.

Abstract

Newspapers are a popular form of written discourse, read by many people, thanks to the novelty of the information provided by the news content in it. A headline is the most widely read part of any newspaper due to its appearance in a bigger font and sometimes in colour print. In this paper, we suggest and implement a method for computing inferences from English news headlines, excluding the information from the context in which the headlines appear. This method attempts to generate the possible assumptions a reader formulates in mind upon reading a fresh headline. The generated inferences could be useful for assessing the impact of the news headline on readers including children. The understandability of the current state of social affairs depends greatly on the assimilation of the headlines. As the inferences that are independent of the context depend mainly on the syntax of the headline, dependency trees of headlines are used in this approach, to find the syntactical structure of the headlines and to compute inferences out of them.

Abstract

In this paper, a dialogue system for Hospital domain in Telugu, which is a resource-poor Dravidian language, has been built. It handles various hospital and doctor related queries. The main aim of this paper is to present an approach for modelling a dialogue system in a resource-poor language by combining linguistic and domain knowledge. Focusing on the question answering aspect of the dialogue system, we identified Question Classification and Query Processing as the two most important parts of the dialogue system. Our method combines deep learning techniques for question classification and computational rule-based analysis for query processing. Human evaluation of the system has been performed as there is no automated evaluation tool for dialogue systems in Telugu. Our system achieves a high overall rating along with a significantly accurate context-capturing method as shown in the results.

Abstract

Anaphora resolution is a challenging task which has been the interest of NLP researchers for a long time. Traditional resolution techniques like eliminative constraints and weighted preferences were successful in many languages. However, they are ineffective in free word order languages like most South Asian languages. Heuristic and rule-based techniques were typical in these languages, which are constrained to context and domain. In this paper, we venture a new strategy using neural networks for resolving anaphora in human-human dialogues. The architecture chiefly consists of three components, a shallow parser for extracting features, a feature vector generator which produces the word embeddings, and a neural network model which will predict the antecedent mention of an anaphora. The system has been trained and tested on Telugu conversation corpus we generated. Given the advantage of the semantic information in word embeddings and appending actor, gender, number, person and part of plural features the model has reached an F1-score of 86.

Abstract

The content on social media now-a-days includes a huge number of messages both in textual and visual forms that are satirical in nature. Satire in the form of irony, sarcasm, and ridicule to a person, is depicted by memes on social media. Satire detection in text is an active area of research, but in the visual domain it is relatively less explored. The objective of our work is detection of satire in images taken from the popular photo-sharing platform - Flickr. Traditional methods for visual satire detection are based on supervised learning which has a necessary requirement of annotating the data which is a tedious task. In our work, to address this issue, we propose a novel, unsupervised approach which leverages the visual semantics of the images. We provide a study of clustering methods, where the difference between visual semantics of the two classes - satirical and non-satirical - becomes the basis for classification of visual content. Here, we suggest an autoencoder based clustering framework to effectively combine embedded feature learning and clustering assignments for detection of satire.

Abstract

The ASVspoof 2019 challenge focuses on countermeasures for all major spoofing attacks, namely speech synthesis (SS), voice conversion (VC), and replay spoofing attacks. This paper describes the IIIT-H spoofing countermeasures developed for ASVspoof 2019 challenge. In this study, three instantaneous cepstral features namely, single frequency cepstral coefficients, zero time windowing cepstral coefficients, and instantaneous frequency cepstral coefficients are used as front-end features. A Gaussian mixture model is used as back-end classifier. The experimental results on ASVspoof 2019 dataset reveal that the proposed instantaneous features are efficient in detecting VC and SS based attacks. In detecting replay attacks, proposed features are comparable with baseline systems. Further analysis is carried out using metadata to assess the impact of proposed countermeasures on different synthetic speech generating algorithm/replay configurations

Abstract

With the growing popularity of social networks, cloud services and online applications, people are becoming concerned about the way companies store their data and the ways in which the data can be applied. Privacy with devices and services operated by the voice are of particular interest. To enable studies in privacy, this paper presents a database which quantifies the experience of privacy users have in spoken communication. We focus on the effect of the acoustic environment on that perception of privacy. Speech signals are recorded in scenarios simulating real-life situations, where the acoustic environment has an effect on the experience of privacy. The acoustic data is complemented with measures of the speakers’ experience of privacy, recorded using a questionnaire. The presented corpus enables studies in how acoustic environments affect peoples’ experience of privacy, which in turn, can be used to develop speech operated applications which are respectful of their right to privacy. Index Terms: Experience of privacy, speech interfaces, speech corpus, acoustic environment, right to privacy

Abstract

In this paper, we propose an online learning algorithm called PRIL for learning ranking classifiers using interval labeled data. We show the correctness of PRIL by showing that it preserves the orderings of the thresholds in successive trials. We show that the proposed algorithm converges in finite number of steps if there exists an ideal classifier. We also give the mistake bound for the general case and provide O(pT) regret bound for the proposed algorithm. We show the effectiveness of PRIL by comparing its performance with other approaches.

Abstract

Majority of the automatic speech recognition systems (ASR) are trained with neutral speech and the performance of these systems are affected due to the presence of emotional content in the speech. The recognition of these emotions in human speech is considered to be the crucial aspect of human-machine interaction. The combined spectral and differenced prosody features are considered for the task of the emotion recognition in the first stage. The task of emotion recognition does not serve the sole purpose of improvement in the performance of an ASR system. Based on the recognized emotions from the input speech, the corresponding adapted emotive ASR model is selected for the evaluation in the second stage. This adapted emotive ASR model is built using the existing neutral and synthetically generated emotive speech using prosody modification method. In this work, the importance of emotion recognition block at the front-end along with the emotive speech adaptation to the ASR system models were studied. The speech samples from IIIT-H Telugu speech corpus were considered for building the large vocabulary ASR systems. The emotional speech samples from IITKGP-SESC Telugu corpus were used for the evaluation. The adapted emotive speech models have yielded better performance over the existing neutral speech models.

Abstract

The easy implementation of replay attacks by a fraudster poses a severe threat to automatic speaker verification (ASV) technology than the other spoofing attacks like speech synthesis and voice conversion. Replay attacks refer to an attack by a fraudster to get illegitimate access to an ASV system by playing back the speech sample collected from genuine target speaker. The significant cues that can differentiate between genuine and replay recordings are channel characteristics. To capture these characteristics, one need to extract features from the spectrum, which should have high spectral and temporal resolutions. Zero time windowing (ZTW) analysis of speech is one such time-frequency analysis technique, which results in high spectral and temporal resolution spectrum at each sampling instant. In this study, new features are proposed by applying cepstral analysis to ZTW spectrum. Experiments are performed on two publicly available replay attack databases namely BTAS 2016 and ASVspoof 2017. The first set of experiments are conducted using Gaussian mixture models to evaluate the potential of proposed features. Performance of the proposed system in terms of half total error rate is 0.75% and in terms of equal error rate is 14.75% on BTAS 2016 and ASVspoof 2017 evaluation sets respectively. A score level fusion is performed by using proposed features with previously proposed single frequency filtering cepstral coefficients. This fused result outperformed the previously reported best results on these two datasets