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.

Abstract

In production of voiced speech, epochs or glottal closure instants (GCIs) refer to the instants of significant excitation of the vocal tract. Extraction of GCIs is used as a pre-processing stage in many areas of speech technology, such as in prosody modification, speech syn- thesis and voice source analysis. In the past decades, several GCI detection algorithms have been developed and most of them provide excellent results for speech signals produced using modal (normal) type of phonation. There are, however, no studies comparing multi- ple state-of-the-art GCI detection methods in emotional speech. In this paper, we compare six GCI detection algorithms using emo- tional speech and known evaluation metrics. We use the Berlin EMO-DB acted emotional speech database which contains seven emotions and simultaneous electroglottography (EGG) recordings as ground truth. The results show that all six GCI detection algo- rithms give best performance in processing speech of neutral emo- tion and that the performance degrade particularly in emotions of high arousal (anger and joy). To improve the performance of GCI detection in emotional speech, the study underlines the importance of local average pitch period estimates.

Abstract

Multi user massive multiple input multiple output (MU-m-MIMO) has emerged as a viable technology for scaling up existing communication systems, and in serving increasing number of users for the next-generation communication systems. Several signal processing algorithms exist for mitigating the performance-limiting artefacts encountered in MU-m-MIMO systems (like inter-symbol interference, inter-channel interfer- ence, and device nonlinearities), among which, reproducing kernel Hilbert space (RKHS) based approaches have emerged to provide effective solutions. However, most of the existing RKHS based detectors for MU-m-MIMO are dictionary-based, which makes it difficult to gauge the memory requirements beforehand, and are prone to error in the presence of noisy observations. Hence, to reduce the computational complexity, a Random Fourier Features (RFF) based parallel detection algorithm is proposed for MU-m-MIMO, that uses decomposed blocks of high dimensional observations, and makes the proposed detector scalable for parallel computation using modern multi- core compute-units at the receivers (which is possible today due to advances in computing). Further, the RFF based explicit feature map to RKHS alleviates the requirement of a dictionary, and facilitates ease of practical implementation. Simulations are performed over realistic MU-m-MIMO systems, which indicates that the proposed approach delivers an acceptable uncoded BER performance, whilst maintaining a finite implementation budget, which makes the proposed approach attractive for implementation. Lastly, the error-rate analysis of the proposed detector is performed, and validated through simulations

Abstract

Most of the applications in speech use mel-frequency spectral coefficients (MFSC) as features as they match the human perceptual mechanism, where the emphasis is given to vocal tract characteristics. But in accent classification, mel-scale distribution of filters may not always be the best representations, e.g., pitch accented languages where the emphasis should be on vocal source information too. Motivated by this, we use end-to-end classification of accents directly from waveforms which will reduce the effort of designing features specific to each corpus. The convolution neural network (CNN) model architecture is designed in such a way that the initial layers exhibit similar operation as in MFSC by initializing the weights using time approximate of MFSC. The entire network along with initial layers is trained to learn accent classification. We observed that learning directly from waveform improved the performance of accent classification when compared to CNN trained on hand-engineered features by 10.94% UAR on the test dataset of common voice corpus. Analyzing the filters after learning, we observed changes in distribution and bandwidths of center frequencies. We further observed the importance of appropriately initializing CNN filters