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Code-switching refers to the phenomena of mixing of words or phrases from foreign languages while communicating in a native language by the multilingual speakers. Codeswitching is a global phenomenon and is widely accepted in multilingual communities. However, for training the language model (LM) for such tasks, a very limited code-switched textual resources are available as yet. In this work, we present an approach to reduce the perplexity (PPL) of Hindi-English code-switched data when tested over the LM trained on purely native Hindi data. For this purpose, we propose a novel textual feature which allows the LM to predict the code-switching instances. The proposed feature is referred to as code-switching factor (CS-factor). Also, we developed a tagger that facilitates the automatic tagging of the code-switching instances. This tagger is trained on a development data and assigns an equivalent class of foreign (English) words to each of the potential native (Hindi) words.

Added on May 9, 2019

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  • Contributed by : Individual
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Ganji Sreeram, Rohit Sinha

An approach to diarize taniavartanam segments of a Carnatic music concert is proposed in this paper. Information bottleneck(IB) based approach used for speaker diarization is applied for this task. IB system initializes the segments to be clustered uniformly with fixed duration. The issue with diarization of percussion instruments in taniavartanam is that the stroke rate varies highly across the segments. It can double or even quadru-ple within a short duration, thus leading to variable information rate in different segments. To address this issue, the IB sys-tem is modified to use the stroke rate information to divide the audio into segments of varying durations. These varying dura-tion segments are then clustered using the IB approach which is then followed by Kullback-Leibler hidden Markov model (KL-HMM) based realignment of the instrument boundaries. Perfor-mance of the conventional IB system and the proposed system is evaluated on standard Carnatic music dataset. The proposed technique shows a best case absolute improvement of 8.2% over the conventional IB based system in terms of diarization error rate.

Added on May 9, 2019

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  • Contributed by : Conssortium
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Nauman Dawalatabad, Jom Kuriakose, C. Chandra Sekhar, Hema A. Murthy

Code-switching or mixing is the use of multiple languages in a single utterance or conversation. Borrowing occurs when a word from a foreign language becomes part of the vocabulary of a language. In multilingual societies, switching/mixing and borrowing are not always clearly distinguishable. Due to this, transcription of code-switched and borrowed words is often not standardized, and leads to the presence of homophones in the training data. In this work, we automatically identify and disambiguate homophones in code-switched data to improve recognition of code-switched speech. We use a WX-based common pronunciation scheme for both languages being mixed and unify the homophones during training, which results in a lower word error rate for systems built using this data.

Added on May 9, 2019

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  • Contributed by : Individual
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Brij Mohan Lal Srivastava, Sunayana Sitaram

Gaussian generative models have been shown to be equivalent to discriminative log-linear models under weak assumptions for acoustic modeling in speech recognition systems. In this paper, we note that the output layer of deep learning model consists of a first-order log-linear model, also known as logistic regression, which induces a set of homoscedastic distributions in the generative model space, resulting in linear decision boundaries.

Added on May 9, 2019

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  • Contributed by : Individual
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Ankit Raj, Shakti P. Rath, Jithendra Vepa

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 Bodo as the target language. There are 70,000 sentences, including Health, Tourism, Agriculture and Entertainment domain in this corpus. This corpus has a unique sentence ID for each sentence, UTF-8 encoding, and text file format. The translated sentences have been POS tagged and Chunked properly. The chunking guideline used in this corpus creation, is provided in supporting document.

Added on April 26, 2019

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