Standardized GUI Framework Using Python for Speech Processing: NLP

Rudrappa, Naveenkumar T.; Reddy, Mallamma V.; Hanumanthappa, M.

doi:10.1109/icecet52533.2021.9698522

Cited by 1 publication

(2 citation statements)

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“…The proposed methodology (22) has resulted in the development of an incremental prototype named as cross lingual speech identification, retrieval and translation (CLSIRT) by utilizing readily available open source software and hardware. The subsystem performs language identification by utilizing a combinatorial model consisting of Hidden-Markov-Model (HMM), Gaussian-Mixture-Model (GMM), Artificial-Neural-Networks (ANN), Deep-Neural-Networks (DNN) for improving speech matching accuracy.…”

Section: Methodsmentioning

confidence: 99%

“…The output of the developed methodology is identification of spoken speech language in real time and in a recorded wave file as shown in Figure 1. The developed sub-system provides a user-friendly graphical interface (22) to record and store small, medium and large speech files of user defined duration on a large storage media in the form of wav file with .wav file extension and can be utilized for further processing. This graphical user interface has laid a benchmark in the field of NLP to capture human speech for a variety of research applications for upcoming researchers as shown in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

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KHiTE: Multilingual Speech Acquisition to Monolingual Text Translation

Rudrappa¹,

Reddy²,

Hanumanthappa³

2023

IJST

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Objectives: To develop a system that accepts cross-lingual spoken reviews consisting of two to four languages, translate to target language text for Indic languages namely Kannada, Hindi, Telugu and/or English termed as cross lingual speech identification and text translation system. Methods: Hybridization of software engineering models are used in natural languages for pre-processing such as noise removal and speech splitting to obtain phonemes. Combinatorial models namely Hidden-Markov-Model, Artificial Neural Networks, Deep Neural Networks and Convulutional Neural Networks were deployed for direct and indirect speech mapping. Trained corpus consisting of thousand phonemes in the form of wave files for each language considered is named as KHiTEShabdanjali. The basic parameters cosidered for training dataset are pause, pitch, sampling frequency, threshold etc. Findings: The research has resulted in the development of mono-lingual and multi-lingual speech identification, tool for processing of cross-lingual speech and language identification, mono-lingual, bi-lingual, tri-lingual and quad-lingual speech to monolingual text translation for the four languages. It is a generic approach and can be used for other regional languages of India by training the corpus with the selected language. Novelty: Cross-lingual speech identification and text translation system helps users in e-shopping by reducing the time incurred in making a decision to purchase a product having enough features at an economical price, e-tutoring, e-farming activities, digitizing, defence etc.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%