Abstract

Energy utilization can be improved by precise plug load monitoring and control. Plug load energy consumption is nearly 30% of the total building energy consumption. Therefore, plug load identification is a key requirement for energy conservation in buildings. Intrusive load monitoring techniques identify loads precisely but have not been tested widely so far for their performance in changing operating conditions. Hence, the present research proposes a robust low frequency intrusive load monitoring technique to identify load accurately. A smart power strip using proposed load identification technique is designed and developed. Linear regression is applied on the acquired data to capture the behavioral trends of a particular device more explicitly and concisely. Further, weighted K-NN classifier is applied on the transformed data set for device. Experimental results show that the proposed algorithm performs better than the standard classifiers, and can offer tangible savings.

Abstract

A high degree of robustness is a prerequisite to operate speech and language processing systems in practical environments. Performance of these systems is highly influenced by varying and mixed background environments. In this pa-per, we put forward a robust method for automatic language identification in various background environments. Combined temporal and spectral processing method is used as a preprocessing technique for enhancing the degraded speech. Language discriminative information in high sonority regions of speech is used for the task of language identification. Sonority regions are regions of speech whose signal energy is high and these regions are less influenced by background environments.Spectral energy of formants in the glottal closure regions is employed as an acoustic correlate for the detection of sonority regions of speech. In this paper performance of the LID system is studied in various background environments like clean room, car factory,high frequency,pink and white noise environments. In this work, Indian Institute of Technology Kharagpur - Multi LingualI ndian Language Speech Corpus (IITKGP-MLILSC) is used for building language identification system. Noise speech samples from the NOISEX database are employed in the present study.The performance of the proposed method is quite satisfactory compared to existing approaches.

Abstract

Most of the Indian languages are originated from Devanagari, the script of the Sanskrit language. In-spite of similarity in phoneme sets, every language its own influence on the phonotactic constraints of speech in that language. A modelling technique that is capable of capturing the slightest variations imparted by the language is a prerequisite for developing a language identification system (LID). Use of Gaussian mixture modelling technique with a large number of mixture components demands a large training data for each language class, which is hard to collect and handle. In this work, phonotactic variations imparted by the different languages are modelled using Gaussian mixture modelling with a universal background model (GMM-UBM) technique.In GMM-UBM based modelling certain amount of data from all the language classes is pooled to develop a universal background model (UBM) and the model is adapted to each class. Spectral features (MFCC) are employed to represent the language specific phonotactic information of speech in different languages. During the present study, LID systems are developed using the speech samples from IITKGP-MLILSC. In this work, performance of the proposed GMM-UBM based LID system is compared with conventional GMM based LID system. An average improvement of 7–8% is observed due to the use of UBM-based modelling of developing a LID system.

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.