Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Kota, S.R.

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    An Improved Transformer Transducer Architecture for Hindi-English Code Switched Speech Recognition
    (International Speech Communication Association, 2022) Antony, A.; Kota, S.R.; Lade, A.; Spoorthy, V.; Koolagudi, S.G.
    Due to the extensive usage of technology in many languages throughout the world, interest in Automatic Speech Recognition (ASR) systems for Code-Switching (CS) in speech has grown in recent years. Several studies have shown that End-to-End (E2E) ASR is easier to adopt and works much better in monolingual settings. E2E systems are likewise widely recognised for requiring massive quantities of labelled speech data. Since there is a scarcity in the availability of large amount of CS speech, E2E ASR takes longer computation time and does not offer promising results. In this work, an E2E ASR model system using a transformer-transducer architecture is introduced for code-switched Hindi-English speech, and also addressed training data scarcity by leveraging the vastly available monolingual data. Specifically, the language-specific modules in the Transformer are pre-trained by leveraging the vastly available single language speech datasets. The proposed method also provides a Word Error Rate (WER) of 29.63% and Transliterated Word Error Rate (T-WER) of 27.42% which is better than the state-of-the-art by 2.19%. © © 2022 ISCA.
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    Machine Learning Based Propagation Loss Module for Enabling Digital Twins of Wireless Networks in ns-3
    (Association for Computing Machinery, 2022) Almeida, E.N.; Rushad, M.; Kota, S.R.; Nambiar, A.; Harti, H.L.; Gupta, C.; Waseem, D.; Santos, G.; Fontes, H.; Campos, R.; Tahiliani, M.P.
    The creation of digital twins of experimental testbeds allows the validation of novel wireless networking solutions and the evaluation of their performance in realistic conditions, without the cost, complexity and limited availability of experimental testbeds. Current trace-based simulation approaches for ns-3 enable the repetition and reproduction of the same exact conditions observed in past experiments. However, they are limited by the fact that the simulation setup must exactly match the original experimental setup, including the network topology, the mobility patterns and the number of network nodes. In this paper, we propose the Machine Learning based Propagation Loss (MLPL) module for ns-3. Based on network traces collected in an experimental testbed, the MLPL module estimates the propagation loss as the sum of a deterministic path loss and a stochastic fast-fading loss. The MLPL module is validated with unit tests. Moreover, we test the MLPL module with real network traces, and compare the results obtained with existing propagation loss models in ns-3 and real experimental results. The results obtained show that the MLPL module can accurately predict the propagation loss observed in a real environment and reproduce the experimental conditions of a given testbed, enabling the creation of digital twins of wireless network environments in ns-3. © 2022 ACM.