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Under the Indian Languages Corpora Initiative phase –II (ILCI Phase-II) project, initiated by the MeitY, Govt. of India, Jawaharlal Nehru University, New Delhi had collected Gujarati monolingual text corpus. There are approx. 30,000 sentences of general domain in this corpus. These sentences have been POS tagged and Chunked properly. The chunking guideline is provided in supporting document. This corpus has following features: unique ID, UTF-8 encoding, and text file format.

Added on April 23, 2019

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  • Contributed by : ILCI Consortium, JNU
  • Product Type : Text Corpora
  • License Type : Research
  • System Requirement : Not Applicable

Under the Indian Languages Corpora Initiative (ILCI) project initiated by the MeitY, Govt. of India, Jawaharlal Nehru University, New Delhi had collected corpus in Hindi as source language and translated it in Gujarati as target language. There are 70,000 sentences including Health, Tourism, Agriculture and Entertainment domain in this corpus. Each sentence has a unique ID. The translated sentences have been POS tagged and Chunked properly. The chunking guideline is provided in supporting document.

Last updated on April 23, 2019

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  • Contributed by : ILCI Consortium, JNU
  • Product Type : Text Corpora
  • License Type : Research
  • System Requirement : Not Applicable

We show an effective way of adding context information to shallow neural language models. We propose to use Subspace Multinomial Model (SMM) for context modeling and we add the extracted i-vectors in a computationally efficient way. By adding this information, we shrink the gap between shallow feed-forward network and an LSTM from 65 to 31 points of perplexity on the Wikitext-2 corpus (in the case of neural 5-gram model). Furthermore, we show that SMM i-vectors are suitable for domain adaptation and a very small amount of adaptation data (e.g. endmost 5% of a Wikipedia article) brings a substantial improvement. Our proposed changes are compatible
with most optimization techniques used for shallow feedforward LMs.

Added on December 19, 2018

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  • Contributed by : Individual
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Karel Benes,Santosh Kesiraju,Lukas Burget

Nasals and approximants consonants are often confused with each other. Despite the distinction in the production mechanism, these two sound classes exhibit a similar low frequency behavior, and lack significant high frequency content. The present study uses a spectral representation obtained using the
zero time windowing (ZTW) analysis of speech, for the task of distinction between these two. The instantaneous spectral representation has good resolution at resonances, which helps to highlight the difference in the acoustic vocal tract system response for these sounds. The ZTW spectra around the regions of glottal closure instants are averaged to derive parameters for their classification in continuous speech.

Added on December 19, 2018

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  • Contributed by : Individual
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : RaviShankar Prasad,Sudarsana Reddy Kadiri,Suryakanth V. Gangashetty,B. Yegnanarayana

Impulse-like characteristics of excitation occur at the glottal closure instant (GCI) due to sharp closure of the vibrating vocal folds in each glottal cycle. The GCIs are detected from the excitation component of the speech signal, and the excitation component is derived using inverse filtering or its variants. In this paper we propose a method for GCI detection based on single frequency filtering (SFF) of the speech signal. The SFF output has high signal-to-noise ratio (SNR) property in speech regions. The variance (across frequency) contour computed from the SFF output show rapid changes around the GCIs, and these rapid changes can be observed even when the speech signal is degraded. Thus the GCI locations can be extracted even from degraded speech using the SFF analysis. The robustness of the method is demonstrated for several cases of degradation of speech signal.

Added on December 19, 2018

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  • Contributed by : Individual
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : G. Aneeja,Sudarsana Reddy Kadiri, B. Yegnanarayana