Using Machine Learning for Speech Extraction and Translation: HiTEK Languages

Rudrappa, Naveenkumar T.; Reddy, Mallamma V.

doi:10.23919/indiacom54597.2022.9763300

Cited by 2 publications

(1 citation statement)

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“…The developed sub-system performs monolingual and multilingual speech language identification and translates it to a selected target language. It is a hybridized mixture of a software engineering methodology called the speech information retrieval cycle, dictionary based translation, cognate matching, document translation to speech, interlingual techniques that incorporate text converted to speech and spoken speech query, query translation due to the development and enhancement of multilingual HiTEK dictionaries from scratch named as (MLCLSRT) (23) which inculcates word co-occurrence pattern and a greedy algorithm for phone translation using cohesion technique. It utilizes multilingual dictionary to implement cross language speech retrieval.…”

Section: Methodsmentioning

confidence: 99%

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%