Journal ArticleDOI
G-Cocktail: An Algorithm to Address Cocktail Party Problem of Gujarati Language Using Cat Boost
TLDR
The proposed algorithm, G- Cocktail, addresses the Cocktail party problem of Indian language, Gujarati by utilizing the power of CatBoost algorithm to classify and identify the voice.About:
This article is published in Wireless Personal Communications.The article was published on 2022-02-10. It has received 1 citations till now. The article focuses on the topics: Medicine.read more
Citations
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Proceedings ArticleDOI
Phase-based Cepstral features for Automatic Speech Emotion Recognition of Low Resource Indian languages
TL;DR: This paper proposes a features-based SER model for automatic speech emotion recognition in low-resource Indian languages mainly the Bengali language that is performing well with an average of 96% emotion recognition efficiency as compared to standard methods.
References
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Proceedings Article
LightGBM: a highly efficient gradient boosting decision tree
TL;DR: It is proved that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size, and is called LightGBM.
Posted Content
CatBoost: unbiased boosting with categorical features
Liudmila Ostroumova Prokhorenkova,Gleb Gusev,Aleksandr Vorobev,Anna Veronika Dorogush,Andrey Gulin +4 more
TL;DR: CatBoost as discussed by the authors is a new gradient boosting toolkit that uses ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features.
Posted Content
Supervised Speech Separation Based on Deep Learning: An Overview.
DeLiang Wang,Jitong Chen +1 more
TL;DR: This paper provides a comprehensive overview of the research on deep learning based supervised speech separation in the last several years, and provides a historical perspective on how advances are made.
Proceedings ArticleDOI
Multilingual Speech Recognition with a Single End-to-End Model
Shubham Toshniwal,Tara N. Sainath,Ron Weiss,Bo Li,Pedro J. Moreno,Eugene Weinstein,Kanishka Rao +6 more
TL;DR: This model, which is not explicitly given any information about language identity, improves recognition performance by 21% relative compared to analogous sequence-to-sequence models trained on each language individually and improves performance by an additional 7% relative and eliminate confusion between different languages.
Proceedings ArticleDOI
From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.
TL;DR: Two new absolute CSR performance measures are introduced: MER (match error rate) and WIL (word information lost), which are a simple approximation to the proportion of word information lost which overcomes the problems associated with the RIL (relative information lost) measure.