Abstract

Code-Mixing (CM) is defined as the embedding of linguistic units such as phrases, words, and morphemes of one language into an utterance of another language. CM is a natural phenomenon observed in many multilingual societies. It helps in speeding-up communication and allows wider variety of expression due to which it has become a popular mode of communication in social media forums like Facebook and Twitter. However, current Question Answering (QA) re-search and systems only support expressing a question in a single language which is an unrealistic and hard proposition especially for certain domains like health and technology. In this paper, we take the first step towards the development of a full-fledged QA system in CM language which is building a Question Classification (QC) system. The QC system analyzes the user question and infers the expected Answer Type (AType). The AType helps in locating and verifying the answer as it imposes certain type-specific constraints.We learn a basic Support Vector Machine (SVM) based QC system for English-Hindi CM questions. Due to the inherent complexities involved in processing CM language and also the unavailability of language processing resources such POS taggers, Chunkers, Parsers, we design our current system using only word-level resources such as language iden-tification, transliteration and lexical translation. To reducedata sparsity and leverage resources available in a resource-rich language, in stead of extracting features directly from the original CM words, we translate them commonly into English and then perform featurization. We created an evaluation dataset for this task and our system achieves an ac-curacy of 63% and 45% in coarse-grained and fine-grained categories of the question taxonomy. The idea of translating features into English indeed helps in improving accuracy over the uni-gram baseline

Abstract

Multilingual spoken dialogue systems have gained prominence in the recent past necessitating the requirement for a front-end Language Identification (LID) system. Most of the existing LID systems rely on modeling the language discriminative information from low-level acoustic features. Due tothe variabilities of speech (speaker and emotional variabilities, etc.), large-scale LID systems developed using low-levelacoustic features suffer from a degradation in the performance. In this approach, we have attempted to model the higher level language discriminative phonotactic information for developing an LID system. In this paper, the input speech signal is tokenized to phone sequences by using a language independent phone recognizer. The language discriminative phonotactic information in the obtained phone sequences are modeled using statistical and recurrent neural network based language modeling approaches. As this approach, relies on higher level phonotactical information it is more robust to variabilities of speech. Proposed approach is computationally light weight, highly scalable and it can be used in complement with the existing LID systems

Abstract

In this paper we introduce a new histogram equalization based contrast enhancement method called High Speed Quantile Based Histogram Equalization (HSQHE) suitable for high contrast digital images. The proposed method is an effective tool to deal with the “mean-shift” problem, which is a usual problem with the histogram equalization based contrast enhancement methods. The main idea of HSQHE is to divide input image histogram into two or more sub-histograms, where segmentation is based on quantile values. Since the histogram segmentation is based on the quantile values, the entire spectrum of gray level will always play an important role in enhancement process. Also the proposed method does not require the recursive segmentation of the histogram as in many other methods, and hence proposed method requires less time for segmentation. The experimental results show that the performance of proposed HSQHE method is better as compared to other existing methods available in literature. Also this method preserves image brightness more accurately than the prevailing state of art and takes less time as compared to the other methods.

Abstract

This paper describes our submission for FIRE 2014 Shared Task on Transliterated Search. The shared task features two sub-tasks: Query word labeling and Mixed-script Ad hoc retrieval for Hindi Song Lyrics. Query Word Labeling is on token level language identification of query words in code-mixed queries and back-transliteration of identified Indian language words into their native scripts. We have developed letter based language models for the token level language identification of query words and a structured perceptron model for back-transliteration of Indic words. The second subtask for Mixed-script Ad hoc retrieval for Hindi Song Lyrics is to retrieve a ranked list of songs from a corpus of Hindi song lyrics given an input query in Devanagari or transliterated Roman script. We have used edit distance based query expansion and language modeling followed by relevance based reranking for the retrieval of relevant Hindi Song lyrics for a given query.