Abstract

In recent past a lot of scientific attention is paid on recognizing the emotional state of the speaker from his speech. Emotion recognition is a challenging task as human emotions are complex, subtle and emotive state in human speech does not persist long. So it is important to study the presence of emotion identifiable information in smaller segments of speech. This study is aimed at studying the presence of emotional specific information with relevance to the position of the word in the utterance. During the present study, spectral features are employed to represent emotion specific information in speech. Spectral features from smaller speech segments of speech based on their position in the utterance are employed to study the presence of emotion in speech. Due to the lack of adequate data in small speech segments to support conventional GMM during the course of present study Gaussian mixture modeling with a universal background model (GMM-UBM) is used for developing a emotion recognition system. Speech data from IITKGP-SESC is used during the course of the present study. During the present study 4 (Anger, Fear, Happy and Neutral) emotions are considered.

Abstract

Emotions in human speech are short lived. In an emotive utterance, the emotive gestures produced due to the emotive state of the speaker persists only to a shorter duration. In this study, the regions of an utterance that are highly influenced by the emotive state of the speaker are detected. These regions are labeled as emotionally significant regions. Data from the detected emotionally significant regions is used for developing an emotion recognition system. Physiological constraints of human speech production system are explored for detecting the emotionally significant regions of an utterance. Spectral features from the detected emotionally significant regions are used to develop an emotion recognition system. A significant improvement in the performance of the emotion recognition system is observed inthe present approach. An average improvement of 11% is in noted owing to the use of data from emotionally significant regions while developing an emotion recognition system. Gaussian mixture modelling (GMM) technique is employed to develop an emotion recognition system. During the present study, speech samples from Berlin emotion speech database (EMO-DB) are used. Four basic emotions such as anger, happy, neutral and fear are considered for study

Abstract

In this paper we propose a technique for spectral envelope estimation using maximum values in the sub-bands of Fourier magnitude spectrum (MSASB). Most other methods in the literature parametrize spectral envelope in cepstral domain such as Mel-generalized cepstrum etc. Such cepstral domain representations, although compact, are not readily interpretable. This difficulty is overcome by our method which parametrizes in the spectral domain itself. In our experiments, spectral envelope estimated using MSASB method was incorporated in the STRAIGHT vocoder. Both objective and subjective results of analysis-by-synthesis indicate that the proposed method is comparable to STRAIGHT. We also evaluate the effectiveness of the proposed parametrization in a statistical parametric speech synthesis framework using deep neural networks.

Abstract

In this paper, we investigate the usage of a continuous representation based approach of the feature vector derived from input text to predict the phone durations in a Text to Speech(TTS) system. We pose the problem of predicting the duration as a data driven statistical transformation from the input text onto the feature space. First we present a method to map both the categorical and numeric features that are typically used into a continuous numeric representation and then model it as a form of Matrix Factorization to improve the representation. The proposed system is evaluated based on Root Mean Squared Error(RMSE) as the objective measure and Mean Opinion Score(MOS) as the subjective measure. We find that the system performs on par with the state of the art duration modeling systems both subjectively and objectively.

Abstract

Query-by-example spoken term detection (QbE-STD) refers to the task of determining the subsequence of a reference which matches with a query, where both the queryand the reference are in audio format. Dynamic time warping(DTW) based techniques are explored to match the two sequences with different lengths in an unsupervised manner. In this paper,a completely unsupervised approach based on Segmental DTW(SDTW), a variant of DTW, is considered for the task of QbE-STD where both reference and query utterances are represented using a sequence of Gaussian posteriorgram vectors. SDTW using two different types of bands i.e., Sakoe-Chiba band and Itakura parallelogram is considered to compare the Gaussian posterior-grams of the query and the reference sequence. The effect o fvarying different local constraints of the DTW algorithm on the performance of SDTW is also analyzed in this paper . Results obtained on MediaEval 2012 dataset indicate that SDTW using a band with variable speaking rate, as in Itakura parallelogram,performs better compared to that of using a band with fixed speaking rate, as in Sakoe-Chiba band, across all variations in local constraints.

Abstract

We present a system employing domain specific dictionaries and features to recognize chemical entities. The system utilizes sentence segmentation, tokenization, feature generation, Conditional Random Field (CRF) training and one post-processing step. The dictio-naries were compiled from PubChem, Wikipedia, ChEMBL, DrugBank,word2vec clusters from US patents belonging to A61K class. We report the evaluation results of the run where development set was not included as part of the training set. The best performing model for CEMP taskh as the micro average precision, recall and F-score values of87.26%,79.98%and83.46%, respectively.

Abstract

Sindhi is an Indo-Aryan language spoken by more than 58 million speakers around the world. It is currently a resource poor language which is harmed by the literature being written in multiple scripts. Though the language is widely spoken,primarily, across two countries, the written form is not standardized. In this paper, we seek to develop resources for basic language processing for Sindhi language, in one of its preferred scripts (Devanagari), because a language that seeks to survive in the modern information society requires language technology products. This paper presents our work on building a stochastic Part-of-Speech tagger for Sindhi-Devanagari using conditional random fields with linguistically motivated features. The paper also discusses the steps taken to construct a part-of-speech annotated corpus for Sindhi in Devanagari script. We have also explained in detail the features that were used for training the tagger, which resulted in a part of speech tagger nearing 92% average accuracy.

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

This paper presents a rule-based reorder-ing approach for English-Hindi machinetranslation. We have used the concept ofpada,from P ̄an.inian Grammar to framethe reordering rules. Apadais a wordform which is ready to participate in asentence. The rules are generic enoughto apply on any English-Indian languagepair. We tested the rules on English-Hindilanguage pair and obtained better compre-hensibility score as compared to GoogleTranslate on the same test set. In assess-ing the effectiveness of the rules on padaswhich are analogous to minimal phrases inEnglish, we achieved upto 93% accuracyon the test data.