Abstract

A fundamental essence for emotional speech analysis towards emotion recognition is a good database. Database collected from natural scenarios consists of spontaneous emotions, but there are several issues in collection of such database. Other than the privacy and legal related concerns, there is no control over environment at the background. As it is difficult to collect data from natural scenarios, many research groups have collected data from semi-natural or designed procedures. In this paper, a new emotional speech database named IIIT-H TEMD (International Institute of Information Technology-Hyderabad Telugu Emotional Database) is collected using designed drama situations from actors and non-actors. Utterances are manually annotated using a hybrid strategy by giving the context to one of the listeners. As some of the data collection studies in the literature recommend for actors, analysis of actors data versus non-actors data is carried out for their significance. The total size of the database is about 5 hours, which makes it an useful resource for the emotional speech analysis.

Abstract

Parkinson’s disease (PD) is a progressive deterioration of the human central nervous system. Detection of PD (discriminating patients with PD from healthy subjects) from speech is a useful approach due to its non-invasive nature. This study proposes to use novel cepstral coefficients derived from the single frequency filtering (SFF) method, called as single frequency filtering cepstral coefficients (SFFCCs) for the detection of PD. SFF has been shown to provide higher spectro-temporal resolution compared to the short-time Fourier transform. The current study uses the PC-GITA database, which consists of speech from speakers with PD and healthy controls (50 males, 50 females). Our proposed detection system is based on the i-vectors derived from SFFCCs using SVM as a classifier. In the detection of PD, better performance was achieved when the ivectors were computed from the proposed SFFCCs compared to the popular conventional MFCCs. Furthermore, we investigated the effect of temporal variations by deriving the shifted delta cepstral (SDC) coefficients using SFFCCs. These experiments revealed that the i-vectors derived from the proposed SFFCCs+SDC features gave an absolute improvement of 9% compared to the i-vectors derived from the baseline MFCCs+SDC features, indicating the importance of temporal variations in the

Abstract

Automatic detection and assessment of voice disorders is important in diagnosis and treatment planning of voice disorders. This work proposes an approach for automatic detection and assessment of voice disorders from a clinical perspective. To accomplish this, a multi-level classification approach was explored in which four binary classifiers were used for the assessment of voice disorders. The binary classifiers were trained using support vector machines with excitation source features, vocal-tract system features, and state-of-art OpenSMILE features. In this study source features namely, glottal parameters obtained from glottal flow waveform, perturbation measures obtained from epoch locations, and cepstral features obtained from linear prediction residual and zero frequency filtered signal were explored. The present study used the Saarbucken voice disorders database to evaluate the performance of proposed approach. The OpenSMILE features namely ComParE and eGEMAPS feature sets shown better performance in terms of classification accuracies of 82.8% and 76%, respectively for voice disorder detection. The combination of excitation source features with baseline feature sets further improved the performance of detection and assessment systems, that highlight the complimentary nature of exciting source features.

Abstract

Formants are resonances of the time varying vocal tract system, and their characteristics are reflected in the response of the system for a sequence of impulse-like excitation sequence originated at the glottis. This paper presents a method to enhance the formants information in the display of spectrogram of the speech signal, especially for high pitched voices. It is well known that in the narrowband spectrogram, the presence of pitch harmonics masks the formant information, whereas in the wideband spectrogram, the formant regions are smeared. Using single frequency filtering (SFF) analysis, we show that the wideband equivalent SFF spectrogram can be modified to enhance the formant information in the display by improving the frequency resolution. For this, we obtain two SFF spectrograms by using single frequency filtering of the speech signal at two closely spaced roots on the real axis in the z-plane. The ratio or difference of the two SFF spectrograms is processed to enhance the formant information in the spectrographic display. This will help in tracking rapidly changing formants and in resolving closely spaced formants. The effect is more pronounced in the case of high-pitched voices, like female and children speech. Keywords: formants, spectrograms, single frequency filtering

Abstract

Twisting of a building about vertical axis increases the shear force demand on lateral force resisting elements and it is not desirable as increased shear force results in brittle failure. Generally, twisting is induced in a building due to eccentricity between centre of Mass (CM) and centre of Stiffness (CS) at diaghragm level. Eccentricity can be induced in a structure due to mass, strenght and stiffness arising out of various construction and design limitations. Code insists to apply design forces calculated according to equivalent static method or response spectrum method at displaced center of Mass (CM) so as to cause design eccentricity between CM and CS. The displaced center of mass resulting from design eccentricity consists of two terms static eccentricity and accidental eccentricity. Also, earthquake ground motion has the ability to induce torsion in a structure. Hence in order to account for all the eccentricities, code suggests 5% accidental eccentricity to be included in design eccentricity. In current work, an adjustment of mass eccentricity is verified. For this purpose, three one storey models are created with 5 % unidirectional mass eccentricity (CM), 5% unidirectional stiffness eccentricity (CS) and 5% unidirectional Mass-Stiffness eccentricity (CM-CS). Linear Increment Dynamic Analysis is performed by considering chamoli earthquake and edge displacements are obtained. Later, twist is calculated based on edge displacements. It is concluded that the same amount of mass and stiffness eccentricity (i.,e 5%) , the twist incurred in stiffness eccentric model is signifcantly higher whereas no twist about vertical axis is noticed in mass eccentric model unless mass eccentricity has significant impact on torsional response, the torsional response of combined CM-CS eccentric model is approximately equal to CS eccentric model. The results conclude that universal adjustment of center of mass will not provide the assumed conservativeness of safety against torsion. Apart from this, few countries guidelines like New Zealand [NZS, 2004] has revised accidental eccentricity from 5% to 10%. Hence, design eccentricity needs revision considering the statistical evaluation of various paramters inducing accidental eccentricities , without adjusting mass centre for every eccentricity.

Abstract

This paper describes Naver Labs Europe’s participation in the Robustness, Chat, and Biomedical Translation tasks at WMT 2020. We propose a bidirectional German ↔ English model that is multi-domain, robust to noise, and which can translate entire documents (or bilingual dialogues) at once. We use the same ensemble of such models as our primary submission to all three tasks and achieve competitive results. We also experiment with language model pre-training techniques and evaluate their impact on robustness to noise and out-of-domain translation. For German, Spanish, Italian, and French to English translation in the Biomedical Task, we also submit our recently released multilingual Covid19NMT model.

Abstract

Word segmentation is a fundamental task for most of the NLP applications. Urdu adopts Nastalique writing style which does not have a concept of space. Furthermore, the inherent non-joining attributes of certain characters in Urdu create spaces within a word while writing in digital format. Thus, Urdu not only has space omission but also space insertion issues which make the word segmentation task challenging. In this paper, we improve upon the results of Zia, Raza and Athar (2018) by using a manually annotated corpus of 19,651 sentences along with morphological context features. Using the Conditional Random Field sequence modeler, our model achieves F1 score of 0.98 for word boundary identification and 0.92 for sub-word boundary identification tasks. The results demonstrated in this paper outperform the state-of- the-art methods.

Abstract

In this study, we propose Mel-weighted single frequency filtering (SFF) spectrograms for dialect identification. The spectrum derived using SFF has high spectral resolution for harmonics and resonances while simultaneously maintaining good time-resolution of some speech excitation features such as impulse-like events. The SFF spectrum can represent speech characteristics such as burst time and glottal closure instances better than the short-time Fourier transform (STFT) spectrum. Our hypothesis is that these intricate representations in the SFF spectrum should help in distinguishing dialects. Therefore, we built a dialect identification system which uses an unsupervised, bottleneck feature representation of the Mel-weighted SFF spectrogram (Mel-SFF spectrogram) with sequence-to-sequence deep autoen- coders. The language invariance of the proposed system was evaluated using two datasets: the UT-Podcast database (English) and the STYRIALECT database (German). The proposed representations gave a relative improvement of 9.47% and 4.69% in unweighted average recall (UAR) compared to the best baseline method on the development and test datasets, respectively, of the UT-Podcast database. The proposed representations also gave a comparable performance to the best baseline method for the STYRIALECT database. In addition, the fusion of the autoencoder bottleneck features computed from the Mel-SFF and Mel-STFT spectrograms improved the overall performance indicating complementary information between these features. By further analyzing the performance of the proposed representation with different utterance lengths using the UT-Podcast database, we observed that the proposed representation performed better on short utterances. The improved performance given by the Mel-weighted SFF spectrogram for recognizing dialects in both databases supports our hypothesis

Abstract

We want to solve the generalized eigenvalue problem by posing it as an optimization problem on manifold. It is based on a constrained truncated-GMRES [ 8] trust-region strategy to optimize the Rayleigh quotient, in the framework of a recently-proposed trust-region scheme on Riemannian manifolds [1–5].

Abstract

Block chain technology provides a decentralized and secure platform for executing transactions. Smart contracts in Ethereum have been proposed as the mechanism to automate legal contracts securely without the involvement of third parties. Yet, there are still several issues to be resolved especially regarding the updating of smart contracts in blockchain as well as the use of blockchain as part of a legal smart contracts system. In this work we propose a methodology and an architecture for building and deploying legal contracts in the blockchain. As the blockchain is immutable, we cannot update the code of the smart legal contracts, but in real life applications updating of contracts is a requirement that cannot be ignored. In this paper we address the problem of contract update by introducing a new versioning system that keeps track of the changes and links the different versions using a linked list. Moreover, we propose a system architecture where the user interface, the application logic and the blockchain are smoothly integrated in a manner that each part of the system contributes for producing a flexible and transparent execution. We show the applicability of our approach by implementing a system for the case of a rental agreement.