An artificially intelligent approach for automatic speech processing based on triune ontology and adaptive tribonacci deep neural networks

Deepak, Gerard; Surya, Deepak; Trivedi, Ishdutt; Kumar, Ayush; Lingampalli, Amrutha; vijayan, Santhana

doi:10.1016/j.compeleceng.2022.107736

Cited by 10 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TriNNOnto hybrid approach to automatic speech recognition combines various other methods such as language, acoustic, and feature modelling based on the use of the Deep Neural Network. The accuracy of the data acquisition strategy Deepak et al [25] was estimated at 98.15% and 95.18% for two datasets: CMUKids and TIMIT respectively, word errors were low. The percentage of recognition accuracy of phonetic features in the lexemes of the Uzbek language is in the range of 75-100%.…”

Section: Using Different Technologies For Phonemic Speech Recognition...mentioning

confidence: 98%

Creation of An Intelligent System for Uzbek Language Teaching Using Phoneme-Based Speech Recognition

Ibragimova

2023

RIA

View full text Add to dashboard Cite

The recent surge in interest to learn the Uzbek language among foreigners has underscored the need for innovative teaching tools. Despite the limited studies on intelligent systems for phonemic speech recognition in the Uzbek context, this research aimed to address this gap. The purpose of this study was to create an intelligent system for teaching the Uzbek language as a foreign language based on the technology of phonemic recognition of speech signals. It was developed an intelligent system for Uzbek language instruction using phonemic speech recognition technology. The approach utilized various methods, including pinpointing challenging phonemes, comparative data analyses, and analytical-synthetic breakdowns of linguistic components, all enhanced by the wavelet transform's signal refinement. The system's precision in recognizing speech signals phoneme-by-phoneme, emphasizing difficult sounds for learners, promises broader AI-driven language study applications. Specifically designed for the Uzbek language, the system achieves an accuracy range of 67% to 95%. This breakthrough not only propels AI-driven language processing but offers a robust tool for improving Uzbek language instruction, especially beneficial for the Turkic language group. Future avenues include its use in computer modeling and automatic speech processing for Turkic languages, solidifying its innovative contribution to AI-driven language teaching.

show abstract

Section: Using Different Technologies For Phonemic Speech Recognition...mentioning

confidence: 98%

Creation of An Intelligent System for Uzbek Language Teaching Using Phoneme-Based Speech Recognition

Ibragimova

2023

RIA

View full text Add to dashboard Cite

show abstract

“…In Informed Machine Learning, prior knowledge is incorporated into the machine learning process at various stages [17]. Prior knowledge is often represented by an ontology that can be used in the feature engineering phase for selection [18,19], extraction [20][21][22], or augmentation [23,24], in order to acquire more relevant features. They can also be used to facilitate the choice of the most suitable model structure [25] or be directly integrated into the machine learning algorithm [26][27][28][29][30][31].…”

Section: Combining Machine Learning and Ontologiesmentioning

confidence: 99%

Objective-Driven Modular and Hybrid Approach Combining Machine Learning and Ontology

Narsis,

Dujardin,

Nicolle

2023

2023 15th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter)

View full text Add to dashboard Cite

Hybrid artificial intelligence is rapidly advancing, particularly in the domain of combining ontology and machine learning models. However, existing approaches in this field still encounter several limitations. Most current works tend to combine a single ontology model with a specific learning algorithm and often have a strong focus on specific application domains, which can complicate system adaptation and generalization.To address these limitations, we introduce in this paper an objectivedriven, hybrid, and modular approach that promotes the integration of multiple machine learning and ontology models. The approach consists of decomposing the studied application into several tasks, each of them using the most appropriate ontological and machine learning models applied to a subset of knowledge and data. Our approach enhances adaptability and flexibility by tailoring artificial intelligence models to specific goals and reasoning requirements, thereby promoting a more effective hybrid artificial intelligence system and enabling the abstraction and reuse of developed solutions in various application domains. The proposed approach is applied in the design of a hybrid artificial intelligence model for the development of a compact all-optical Arithmetic and Logic Unit.

show abstract

KMetaTagger: A Knowledge Centric Metadata Driven Hybrid Tag Recommendation Model Encompassing Machine Intelligence

Ashvanth

Deepak

Priyadarshini

et al. 2023

Intelligent Systems Design and Applications

View full text Add to dashboard Cite

An artificially intelligent approach for automatic speech processing based on triune ontology and adaptive tribonacci deep neural networks

Cited by 10 publications

References 5 publications

Creation of An Intelligent System for Uzbek Language Teaching Using Phoneme-Based Speech Recognition

Creation of An Intelligent System for Uzbek Language Teaching Using Phoneme-Based Speech Recognition

Objective-Driven Modular and Hybrid Approach Combining Machine Learning and Ontology

KMetaTagger: A Knowledge Centric Metadata Driven Hybrid Tag Recommendation Model Encompassing Machine Intelligence

Contact Info

Product

Resources

About