A Comparative Analysis of the Socio-Demographic Development of the Cities in Siberia and Amazonia in the second half of the 20th century

The article examines the prospects of using a comparative analysis in the study of the socio-demographic processes in the Northeastern regions of Brazil (Amazonia) and Siberia. The authors hypothesize that one of the optimal cases for comparison with Siberia is Amazonia. A comparative analysis suggests that the compared cases (a set of objects, phenomena and processes) should have a number of similar and distinctive features, while at the same time having tangible differences. Despite the seeming heterogeneity, Amazonia and Siberia have many similarities. Among the elements of similarity, the frontier position stands out in the first place. Both Siberia and Amazonia were the regions of European colonization. Throughout the 20th century, Siberia continued to be a relatively undeveloped territory, in fact, still being a frontier. Brazil's Amazon plays a similar role. Both regions have a similar economic profile and act as resource regions that are rich in timber, metals and other minerals as well as having similar energy profiles. The article highlights that the development in both regions peaked in the second half of the 20th century, and also discusses similar phenomena in the demographic processes in these regions. The findings have allowed the authors to formulate the main directions for further comparative analysis of the socio-demographic development of the large urban centers in Siberia and Amazonia

State-of-the-art Speech Recognition With Sequence-to-Sequence Models

Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network. In previous work, we have shown that such architectures are comparable to state-of-theart ASR systems on dictation tasks, but it was not clear if such architectures would be practical for more challenging tasks such as voice search. In this work, we explore a variety of structural and optimization improvements to our LAS model which significantly improve performance. On the structural side, we show that word piece models can be used instead of graphemes. We also introduce a multi-head attention architecture, which offers improvements over the commonly-used single-head attention. On the optimization side, we explore synchronous training, scheduled sampling, label smoothing, and minimum word error rate optimization, which are all shown to improve accuracy. We present results with a unidirectional LSTM encoder for streaming recognition. On a 12, 500 hour voice search task, we find that the proposed changes improve the WER from 9.2% to 5.6%, while the best conventional system achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to 5% for the conventional system.Comment: ICASSP camera-ready versio

Parallel Prim’s

algorithm on dense graphs with a novel extensio

Fast Speaker Diarization Using a High-Level Scripting Language

Abstract—Current state-of-the-art speaker diarization systems use agglomerative clustering of Gaussian Mixture Models (GMMs) to determine the number of speakers in an audio recording. GMM training is a central computation in the agglomerative clustering approach, which presents computational challenges that limit performance and make real-time processing of audio very difficult. With the emergence of highly parallel multicore and manycore processors such as Graphics Processing Units (GPUs), we can re-implement GMM training for these processors to achieve faster than real-time performance by taking advantage of parallelism in the training computation. However, developing and maintaining the low-level GPU code is difficult and requires deep understanding of hardware architecture of the parallel processor. In this paper we present a speaker diarization application captured in under 50 lines of Python that achieves 50-200 × faster than real-time performance by automatically executing computationally intensive GMM training on an NVIDIA GPU with no significant loss in accuracy. I