Abstract

Automatic speaker verification systems are more vulnerable to spoofing attacks. Recently, various countermeasures have been developed for detecting high technology attacks such as speech synthesis and voice conversion. However, there is a wide gap in dealing with replay attacks. In this paper, we propose a new feature for replay attack detection based on single frequency filtering (SFF), which provides high temporal and spectral resolution at each instant. Single frequency filtering cepstral coefficients (SFFCC) with Gaussian mixture model classifier is used for the experimentation on the standard BTAS-2016 corpus. The previously reported best result, which is based on constant Q cepstral coefficients (CQCC) has achieved a half total error rate of 0.67% on this data-set. Our proposed method outperforms the state of the art (CQCC) with a half total error rate of 0.0002%.

Abstract

We describe the system submitted to IBEREVAL-2017 for stance and gender detection in tweets on Catalan Independence [1]. We developed a supervised system using Support Vector Machines with radial basis function kernel to identify the stance and gender of the tweeter using various character level and word level features. Our system achieves a macro-average of F-score(FAVOR) and F-score(AGAINST) of 0.46 for stance detection in both Spanish and Catalan and an accuracy of 64.85% and 44.59% for Gender detection in Spanish and Catalan respectively.

Abstract

The ASVspoof 2017 challenge is about the detection of replayed speech from human speech. The proposed system makes use of the fact that when the speech signals are replayed, they pass through multiple channels as opposed to original recordings. This channel information is typically embedded in low signal to noise ratio regions. A speech signal processing method with high spectro-temporal resolution is required to extract robust features from such regions. The single frequency filtering (SFF) is one such technique, which we propose to use for replay attack detection. While SFF based feature representation was used at front-end, Gaussian mixture model and bi-directional long short-term memory models are investigated at the backend as classifiers. The experimental results on ASVspoof 2017 dataset reveal that, SFF based representation is very effective in detecting replay attacks. The score level fusion of back end classifiers further improved the performance of the system which indicates that both classifiers capture complimentary information

Abstract

We investigate the problem of parsing conversational data of morphologically-rich languages such as Hindi where argument scrambling occurs frequently. We evaluate a state-of-the-art non-linear transition-based parsing system on a new dataset containing 506 dependency trees for sentences from Bollywood (Hindi) movie scripts and Twitter posts of Hindi monolingual speakers. We show that a dependency parser trained on a newswire treebank is strongly biased towards the canonical structures and degrades when applied to conversational data. Inspired by Transformational Generative Grammar (Chomsky, 1965), we mitigate the sampling bias by generating all theoretically possible alternative word orders of a clause from the existing (kernel) structures in the treebank. Training our parser on canonical and transformed structures improves performance on conversational data by around 9% LAS over the baseline newswire parser.

Abstract

In this paper, we report our participation in the Task 2: Triple Scoring of WSDM Cup challenge 2017. In this task, we were provided with triples of "type-like" relations which were given human-annotated relevance scores ranging from 0 to 7, with 7 being the "most relevant" and 0 being the "least relevant". The task focuses on two such relations: profession and nationality. We built a system which could automatically predict the relevance scores for unseen triples. Our model is primarily a supervised machine learning based one in which we use well-designed features which are used to a make a Logistic Ordinal Regression based classification model. The proposed system achieves an overall accuracy score of 0.73 and Kendall’s tau score of 0.36.

Abstract

This paper presents the ongoing process in the design of the first phase of the phonetically balanced codemixed corpus of Hindi-English speech (PBCM-Phase I). The reference corpus is a large code-mixed (LCM) newspaper corpus selected from the sections that contain frequent English insertions in a matrix of Hindi sentence. From a phonetically transcribed corpus, compulsory inclusion of lowest frequency triphones has been ensured, with the assumption that high frequency phones may automatically be included. A high correlation of 0.81 with the representative large corpus has been observed. A small scale speech corpus of 5.6 hours has been collected, by the contribution of 4 volunteer native Hindi speakers. The recording has been conducted in a professional recording studio environment. As a second contribution, this paper also presents a baseline recognition system with pooled monolingual and codemixed speech datasets as training and testing environments.

Abstract

n this paper, we present a generic semi-supervised Word Sense Disambiguation(WSD) method. Currently, the existingWSD methods extensively use domain re-sources and linguistic knowledge.Ourproposed method extractscontext basedlistsfrom a small sense-tagged and un-tagged training data without using do-main knowledge. Experiments on Hindiand Marathi Tourism and Health domainsshow that it gives good performance with-out using any language specific linguisticinformation except the sense IDs presentin the sense-tagged training set and workswell even with small training data by han-dling the data sparsity issue. Other ad-vantages are that domain expertise is notneeded for crafting and selecting featuresto build the WSD model and it can handlethe problem of non availability of match-ing contexts in sense-tagged training set.It also finds sense IDs of those test wordswhich are not present in sense-tag

Abstract

Sentiment analysis in a resource scarcelanguage is a tedious task. We pro-pose a novel method for transfer learn-ing from a target language to English.Our system doesn’t rely on labeleddata for the target language but in-stead links itself onto already existingand extensively labeled word-level lex-ical resource in English (ESWN1) anda semantic parser. Our proposed sys-tem transparently needs no target lan-guage sentiment corpus, and exploitscomplex linguistic structure of the tar-get language for sentiment prediction.This cross lingual approach gives netaccuracy as 83.6%, an improvement of5.4% over the baseline system.

Abstract

Trivia is any fact about an entity which is interesting due to its unusualness, uniqueness or unexpectedness. Trivia could be successfully employed to promote user engagement in various product experiences featuring the given entity. A Knowledge Graph (KG) is a semantic network which encodes various facts about entities and their relationships. In this paper, we propose a novel approach called DBpedia Trivia Miner (DTM) to automatically mine trivia for entities of a given domain in KGs. The essence of DTM lies in learning an Interestingness Model (IM), for a given domain, from human annotated training data provided in the form of interesting facts from the KG. The IM thus learnt is applied to extract trivia for other entities of the same domain in the KG. We propose two different approaches for learning the IM - a) A Convolutional Neural Network (CNN) based approach and b) Fusion Based CNN (F-CNN) approach which combines both hand-crafted and CNN features. Experiments across two different domains - Bollywood Actors and Music Artists reveal that CNN automatically learns features which are relevant to the task and shows competitive performance relative to hand-crafted feature based baselines whereas FCNN significantly improves the performance over the baseline approaches which use hand-crafted features alone. Overall, DTM achieves an F1 score of 0.81 and 0.65 in Bollywood Actors and Music Artists domains respectively.

Abstract

The paper presents an automatic speech recognition (ASR) system for code-mixed read speech in Hindi-English, developed upon the extrapolation of monolingual training resources. A monolingual Hindi acoustic model, mixed with code-mixed speech data has been implemented to train a neural network based speech recognition framework. The testing corpus also follows a similar structure: containing data from both monolingual and code-mixed speech. The shared phonetic transcription, captured in WX notation has been exploited to harness the commonality between the pooled phonesets of Hindi and English. The experiments have been conducted in two separate formulations of a trigram based language model 1) In the first experiment, the language model contains no out-of-vocabulary words, as the test utterances are included in the training of the language model. The word error rate in this case has been obtained to be 10.63 %. 2) In the second experiment, the testing utterances have been excluded from the training language model. The word error rate in this case has been obtained to be 41.66 %.