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 %.

Abstract

We present an approach for POS tag-ging with out any labeled data. Ourmethod requires translated sentencesfrom a pair of languages. We usedfeature transfer from a resource richlanguage to resource poor languages.Across 8 different Indian Languages,we achieved encouraging accuracieswithout any knowledge of the targetlanguage and any human annotation.This will help us in creating annotatedcorpora for

Abstract

Neural Machine Translation is emerg-ing as the de facto standard for Ma-chine Translation across the globe.Statistical Machine Translation hasbeen the state-of-the-art for translationamong Indian languages. This paperprobes into the effectiveness of NMTfor Indian languages and compares thestrengths and weaknesses of NMT withSMT through a vis-a-vis qualitative es-timation on different linguistic param-eters. We compare the outputs ofboth models for the languages English,Malayalam and Hindi; and test themon various linguistic parameters. Weconclude that NMT works better inmost of the settings, however there isstill immense scope for the bettermentof accuracy for translation of IndianLanguages. We describe the challengesfaces especially when dealing with lan-guages from different language families

Abstract

Data sparsity is a key problem incontemporary neural machine transla-tion (NMT) techniques, especially forresource-scarce language pairs. NMTmodels when coupled with large,high quality parallel corpora providepromising results and are an emergingalternative to phrase-based StatisticalMachine Translation (SMT) systems.A solution to overcome data sparsitycan facilitate leveraging of NMT mod-els across language pairs, thereby pro-viding high quality translations de-spite the lack of large parallel cor-pora. In this paper, we demonstrate athree-phase integrated approach whichcombines weakly supervised and semi-supervised learning with NMT tech-niques to build a robust model usinga limited amount of parallel data. Weconduct experiments for five languagepairs (thereby generating ten systems)and our results show a substantial in-crease in translation quality over abaseline NMT model trained only

Abstract

With the advent of word representations, word similarity tasks are becoming increasing popular as an evaluation metric for the quality of the representations. In this paper, we present manually annotated monolingual word similarity datasets of six Indian languages – Urdu, Telugu, Marathi, Punjabi, Tamil and Gujarati. These languages are most spoken Indian languages worldwide after Hindi and Bengali. For the construction of these datasets, our approach relies on translation and re-annotation of word similarity datasets of English. We also present baseline scores for word representation models using state-of-the-art techniques for Urdu, Telugu and Marathi by evaluating them on newly created word similarity datasets.

Abstract

In this paper, we propose efficient and less resource-intensive strategies for parsing of code-mixed data. These strategies are not constrained by in-domain annotations, rather they leverage pre-existing monolingual annotated resources for training. We show that these methods can produce significantly better results as compared to an informed baseline. Due to lack of an evaluation set for code-mixed structures, we also present a data set of 450 Hindi and English code-mixed tweets of Hindi multilingual speakers for evaluation.

Abstract

Language identification (LID) is vital frontend for spoken dialogue systems operating in diverse linguistic settings to reduce recognition and understanding errors. Existing LID systems which use low-level signal information for classification do not scale well due to exponential growth of parameters as the classes increase. They also suffer performance degradation due to the inherent variabilities of speech signal. In the proposed approach, we model the language-specific phonotactic information in speech using recurrent neural network for developing an LID system. The input speech signal is tokenized to phone sequences by using a common language-independent phone recognizer with varying phonetic coverage. We establish a causal relationship between phonetic coverage and LID performance. The phonotactics in the observed phone sequences are modeled using statistical and recurrent neural network language models to predict language-specific symbol from a universal phonetic inventory. Proposed approach is robust, computationally light weight and highly scalable. Experiments show that the convex combination of statistical and recurrent neural network language model (RNNLM) based phonotactic models significantly outperform a strong baseline system of Deep Neural Network (DNN) which is shown to surpass the performance of i-vector based approach for LID. The proposed approach outperforms the baseline models in terms of mean F1 score over 176 languages. Further we provide significant information-theoretic evidence to analyze the mechanism of the proposed approach.

Abstract

Thispaperpresentsourworktoapplynonlinearneuralnetworkforparsingfiver esourcep oorI ndianL anguagesbe-longingtotwomajorlanguagefamilies-Indo-AryanandDravidian.BengaliandMarathiareIndo-AryanlanguageswhereasKannada,TeluguandMalayalambelongtotheDravidianfamily.WhilelittleworkhasbeendonepreviouslyonBengaliandTelugulineartransition-basedparsing,wepresentoneofthefirstparsersforMarathi,KannadaandMalayalam.AlltheIndianlanguagesarefreewordorderandrangefrombeingmoderatetoveryrichinmorphology.Thereforeinthisworkweproposetheusageoflinguisticallymo-tivatedmorphologicalfeatures(suffixandpostposition)inthenonlinearframework,tocapturetheintricaciesofboththelan-guagefamilies.Wealsocapturechunkandgender,number,personinformationelegantlyinthismodel.Weputforwardwaystorepresentthesefeaturescosteffe

Abstract

Recent improvement in usage of digital media has led people to share their opinions about specific entity through audio. In this paper, an approach to detect the sentiment of an online spoken reviews based on relative prosody features is presented. Most of the existing systems for audio based sentiment analysis use conventional audio features, but they are not problem specific features to extract the sentiment. In this work, relative prosody features are extracted from normal and stressed regions of audio signal to detect the sentiment. Stressed regions are identified using the strength of excitation. Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) classifiers are used to build the sentiment models. MOUD database is used for the proposed study. Experimental results show that, the rate of detecting the sentiment is improved with relative prosody features compared with the prosody and Mel Frequency Cepstral Coefficients (MFCC) because the relative prosody features has more sentiment specific discrimination compared to prosody features.

Abstract

Hindi and Urdu are spoken primarily in northern India and Pakistan; together, they constitute the third largest language spoken in the world. 1 They are two standard- ized registers of what has been called the Hindustani language, which belongs to the Indo-Aryan language family. Masica [1993] states that, while they are different languages officially, they are not even different dialects or subdialects in a linguistic sense. Rather, they are different literary styles based on the same linguistically defined