Speech to text conversion for multilingual languages

Ghadage, Yogita H.; Shelke, Sushama

doi:10.1109/iccsp.2016.7754130

Cited by 39 publications

(9 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ghadage et al [10] proposed a speech-to-text conversion system that is purposefully designed to extract, characterize, and identify information from spoken language signals. In the context of this research paper, the system leverages the Mel-Frequency Cepstral Coefficient (MFCC) feature extraction method, along with the Minimum Distance Classifier and Support Vector Machine (SVM) techniques for speech classification.…”

Section: Module 3: Speech To Text Conversionmentioning

confidence: 99%

Exploring Advances in Meeting Minutes Generation and Face Attendance Systems: A Comprehensive Literature Survey

Mane

2024

IJSREM

View full text Add to dashboard Cite

The existing literature survey offers a thorough exploration of automatic text summarization, speech-to-text conversion, and face recognition technologies, all of which are integral to the proposed model named as ConvoLogix. Historically, traditional methods for managing meetings and collaboration involved manual note-taking and attendance tracking. These processes were time-consuming, error-prone, and not conducive to optimization. In today’s fast-paced business environment, the absence of automated solutions hinders efficiency and collaboration. The survey underscores the significance of embracing advanced techniques in Machine Learning with Traditional and Deep Learning Models for Audio and video processing for process automation, emphasizing their pivotal role in streamlining meetings and attendance tracking. A key theme within the survey is the identification of limitations associated with conventional approaches to meeting minute generation and attendance recording. The ConvoLogix model’s core objective is to leverage these technologies to automate meeting minutes generation and attendance tracking, resulting in time savings, improved collaboration, and data-driven insights. Key Words: Automated Meeting Summarization, Face Attendance Tracking, Natural Language Processing, Machine Learning, Deep Learning, Neural Network Models

show abstract

Section: Module 3: Speech To Text Conversionmentioning

confidence: 99%

Exploring Advances in Meeting Minutes Generation and Face Attendance Systems: A Comprehensive Literature Survey

Mane

2024

IJSREM

View full text Add to dashboard Cite

show abstract

“…The PASCAL-2005 contest (Hu W, 2014) [6] , held in 2005, was an early contest to propose the question of text analysis and retrieval. The dataset released by the contest gave birth to many early studies in this eld, such as similarity-based methods (Paul C, 2016) [7] , text alignment based methods (Voeste A, 2021) [8] , logic calculus based methods (Waszek D, 2021) [9] and text transformation based methods (Ghadage Y H, 2016) [10] . In the similarity-based approach, Park designed a word bag-based text implication model (Park B, 2018) [11] .…”

Section: Related Workmentioning

confidence: 99%

Design of intelligent legal text analysis and information retrieval system based on BERT model

Li,

Wang

2023

Preprint

View full text Add to dashboard Cite

The amalgamation of information retrieval systems and soft computing techniques establishes a robust framework to confront the challenges and seize the opportunities presented by the vast expanse of big data. As the volume, diversity, and velocity of data continue to proliferate, further advancements in this domain are poised to significantly contribute to various realms, encompassing healthcare, finance, e-commerce, and scientific research, ultimately propelling innovation and facilitating decision-making in the epoch of big data. The application of artificial intelligence (AI) technology to the analysis and comprehension of legal documents holds the potential to expedite the acquisition of case-specific information by legal researchers, thereby expediting their research endeavors. This paper proposes a legal text analysis and retrieval model, rooted in the Bleem model (Bert-based Legal Paper's Element Extracting Model). Initially, our model employs Bert as the coding layer to extract the semantic information embedded within document sentences and element exemplars. Subsequently, we leverage the Attention mechanism to align the semantic essence of element example sentences with document sentences, while simultaneously computing their respective attention weights. Comparative experiments and attention visualization are then employed to validate the efficacy of the Bleem model. The experimental results corroborate the superiority of the Bleem model in terms of accuracy and F1 scores. The visualization of the attention mechanism effectively reveals the inner workings of the Bleem model and unveils its capacity to explore pertinent fragments within document sentences and element examples. Building upon the aforementioned model, we have devised an intelligent legal text analysis and retrieval system, empowering legal researchers to swiftly acquire pivotal data through case briefs. This application has effectively fostered the integration of legal services within the holistic management of public risks.

show abstract

“…In (Ghadage et al, 2016), the authors have designed a multi-language speech-to-text conversion system. It was focused on Marathi -Indian, English, Marathi-English mix speech using Mel-Frequency Cepstrum Coefficients (MFCC) technique for feature extraction.…”

Section: Speech-to-textmentioning

confidence: 99%

“…For example, the authors in (Alkhatib et al, 2017) and (Yousfi et al, 2016) introduced only the Holy Qur'an recitation. In (Ghadage et al, 2016), the authors designed a multi-language speech-to-text conversion system focusing on Marathi -Indian-English, Marathi-English to extract, characterize and recognize the information about speech. However, all the presented studies and surveys investigated either the recitation of the Holy Qur'an in the Arabic language or the recitation of other different languages.…”

mentioning

confidence: 99%

A new framework for Arabic recitation using speech recognition and the Jaro Winkler algorithm

Marie-Sainte

2021

KJS publishes peer-review articles in Mathematics, Computer Sci

View full text Add to dashboard Cite

Automated recitation plays an important role in improving self-learning. It is based on Speech/Text recognition. The research in Arabic speech recognition is very limited. The few existing applications are only based on the Holy Qur’an. This article proposed a new system (Samee’a - ) to facilitate memorizing any kind of text such that poems, speeches and the Holy Qur’an. Samee’a system is based on Google Cloud Speech Recognition API to convert the Arabic speech to text and Jaro Winkler Distance algorithm to determine the similarity between the original and converted texts. The system has been tested using 70 collected files ranging between 12 to 400 words and some chapters from the Holy Qur’an. The average similarity achieved 83.33% for the 70 files and 69% for the selected chapters of the Holy Qur’an. These results were enhanced to 91.33 % and 95.66% after applying preprocessing operations on the text files and the Holly Qur’an respectively. To validate the obtained results, two comparison studies were performed. The Jaro Winker distance was successfully compared to the cosine and the Euclidean distance. In addition, the proposed system outperformed the related work with an improvement of the similarity reaching 5% when using section 30 of the Holy Qur’an. Finally, the user experience testing was carried out by 10 users of different ages (between 5 and 50-year-old) using small texts and some small chapters of the Holy Qur’an. The proposed system proved its efficiency.

show abstract

Speech to text conversion for multilingual languages

Cited by 39 publications

References 4 publications

Exploring Advances in Meeting Minutes Generation and Face Attendance Systems: A Comprehensive Literature Survey

Exploring Advances in Meeting Minutes Generation and Face Attendance Systems: A Comprehensive Literature Survey

Design of intelligent legal text analysis and information retrieval system based on BERT model

A new framework for Arabic recitation using speech recognition and the Jaro Winkler algorithm

Contact Info

Product

Resources

About