Language Identification Research Articles

Convolutional neural network based language identification system: A spectrogram based approach

Convolutional neural network based language identification system: A spectrogram based approach

Enhancing human computer interaction with coot optimization and deep learning for multi language identification

Human-Computer Interaction (HCI) is a multidisciplinary field focused on designing and utilizing computer technology, underlining the interaction interface between computers and humans. HCI aims to generate systems that allow consumers to relate to computers effectively, efficiently, and pleasantly. Multiple Spoken Language Identification (SLI) for HCI (MSLI for HCI) denotes the ability of a computer system to recognize and distinguish various spoken languages to enable more complete and handy interactions among consumers and technology. SLI utilizing deep learning (DL) involves using artificial neural networks (ANNs), a subset of DL models, to automatically detect and recognize the language spoken in an audio signal. DL techniques, particularly neural networks (NNs), have succeeded in various pattern detection tasks, including speech and language processing. This paper develops a novel Coot Optimizer Algorithm with a DL-Driven Multiple SLI and Detection (COADL-MSLID) technique for HCI applications. The COADL-MSLID approach aims to detect multiple spoken languages from the input audio regardless of gender, speaking style, and age. In the COADL-MSLID technique, the audio files are transformed into spectrogram images as a primary step. Besides, the COADL-MSLID technique employs the SqueezeNet model to produce feature vectors, and the COA is applied to the hyperparameter range of the SqueezeNet method. The COADL-MSLID technique exploits the SLID process’s convolutional autoencoder (CAE) model. To underline the importance of the COADL-MSLID technique, a series of experiments were conducted on the benchmark dataset. The experimentation validation of the COADL-MSLID technique exhibits a greater accuracy result of 98.33% over other techniques.

Machine-Based and Auditory Identification of Slavic Languages

This paper presents a comparison of auditory and machine-based identification of linguistic origins. Two studies were conducted to assess the ability of lay listeners and a stateof-the-art machine approach to identify Slavic L1 from delexicalized speech samples. The first study involved 228 native speakers of the four Slavic languages (Bulgarian, Czech, Polish and Russian) who had not received any prior training in Slavic philology, phonetics, linguistics, or forensic science. Their task was to identify the linguistic origins of speakers when exposed to limited phonetic cues. The stimuli consisted of meaningless logatomes to control for the lexical information. The second study employed machine-based identification of a spoken language, based on two distinct approaches: (1) formant structure of phonetic signal and (2) a neural network and vector representation of speech samples. The data showed that Slavic native speakers, even when exposed to limited auditory cues, are able to identify speakers’ L1s. Interestingly, in the context of the Bulgarian language, the machine-based identification method performed better than the lay listeners. The results of the experiments provide insight into the advantages of hybrid approaches in investigations related to LADO (Language Analyses for the Determination of Origin). Furthermore, the outcomes of this comparison may contribute to the debate on the involvement of native speakers in L1 identification procedures for closely related languages.

Kurdish Sign Language Recognition Using Convolutional Neural Network (CNN)

The present study examines the obstacles encountered by the deaf population, with a specific emphasis on the increasing significance of sign language in facilitating effective communication. The main mode of communication for deaf persons is Sign Language (SL), which is a visual and expressive method of communicating meaning via facial expressions, hand motions, and body gestures. The objective of this project is to automate the identification of sign language in order to improve accessibility and decrease reliance on interpreters. Specifically, the work focused on constructing an alphabet recognition system for Kurdish Sign Language (KSL). Due to its many intricacies and resemblances to the Arabic script, KSL requires a strong recognition model. The proposed method utilizes Convolutional Neural Networks (CNN) with an actual dataset to accurately recognize both numerical values and alphabetic characters in the Korean Sign Language (KSL). Real-time operation allows for rapid recognition of hand gestures and subsequent provision of textual output. The collection comprises 132,000 hand pictures, including 33 alphabetica and (0-9) numeral signs. Significantly, the use of MediaPipe, a method for processing 3D images, greatly improves the efficiency of detection. Multiple methodologies were investigated, and the integration of Convolutional Neural Networks (CNN), TensorFlow, and MediaPipe resulted in a remarkable accuracy of 99.87% with negligible dropout rates. This study establishes a foundation for enhanced communication and independence for the deaf population, representing a notable advancement in the automation of sign language recognition.

Metacognitive Awareness in Second Language Learning: Displaced Primary School Students’ Experiences of Learning German Upon Transition to the Target Language Environment

Drawing on international pedagogical discourse on the inclusion of ethnically diverse refugee groups in the educational systems of OECD countries as well as our previous research on the Ukrainian refugee perspectives on primary school in Switzerland, the author examines experiences of second language acquisition by displaced Ukrainian children in Swiss primary schools. The two-year research period allowed for the exploration of dynamics of German language acquisition by displaced children in conditions of full immersion in the target language environment and identification of the factors influencing their second language (German) learning and performance. The involvement of a Ukrainian researcher in the research project ensured accessibility of the target group and effective researcher-respondent communication in the course of longitudinal study carried out within the framework of a qualitative methodology with data collection through narrative inquiry and participant observations as research tools. The study identifies the effects of the two groups of factors: learner-internal (motivation, metacognitive skills, prior knowledge, and learning strategies) and learner-external ones (second language learning context, type of second language instruction available upon displacement, etc.). Metacognitive skills proved to be instrumental in second language learning in the context of the learners’ unexpected transition to a new educational setting and full immersion in the target language environment. Effects of metacognitive skills on second language learning of the studied group of displaced children vary considerably depending on the student’s age, prior knowledge and language learning experiences, and second language instruction available upon the displacement. The multidisciplinary character of the second language acquisition research undertaken by the author allows for diverse practical applications of its results in second language pedagogy, psychology of education, as well as in refugee and primary education.

A survey on NLP tasks, resources and techniques for low-resource Telugu-English code-mixed text

With the proliferation of informal content on various social media platforms in the form of posts, comments, and feedback, the importance of analyzing text in code-mixed form is gaining importance. Telugu, a low-resource Indian language, has a lot of online content being generated in code-mixed form. However, the lack of large corpora, annotated data and Natural Language Processing (NLP) resources are impeding research on Telugu-English code-mixed data. This paper provides a survey of existing literature on Telugu-English code-mixed text in the areas of resources, POS tagging, Named Entity Recognition, language identification, sentiment analysis, application tasks, dialog systems, and Question-Answering. Various datasets being used by the researchers in the field, along with methods applied to them are detailed. Research gaps are identified to provide future direction for researchers working in this field.

Pengembangan Aplikasi Sistem Pakar Preferensi ‘Bahasa Cinta’ Manusia Berbasis Web

The purpose of this research is to produce a web-based expert system application to identify a person's love language using the nave Bayes classifier method. This expert system application to identify a person's love language is intended for people who have not realized how important it is to know which love language is relevant to them. This expert system application to identify a person's love language provides guidance in the form of questions about the type of love language. The answers to the questions asked will lead to the final decision on the results of the expert system diagnosis. Sources of expert system application knowledge base obtained from books and the internet. In making the application using the extreme programming software development method. Extreme programming consists of four stages, namely: planning, design, coding, and testing. The result of this research is the application of an expert system of love language identification that works on a web base.

Cuneiform Text Dialect Identification Using Machine Learning Algorithms and Natural Language Processing (NLP)

Due to a lack of resources and the tokenization issue, it is challenging to identify the languages inscribed in cuneiform symbols. Sumerian and six dialects of the Akkadian language-Old Babylonian, Middle Babylonian Peripheral, Standard Babylonian, Neo-Babylonian, Late Babylonian, and Neo-Assyrian-are among the seven languages and dialects written in cuneiform that need to be identified. This problem is addressed by the Cuneiform Language Identification task in VarDial 2019. This paper presents ten machine learning algorithms derived from four types of machine learning that were used (supervised, ensemble, instance-based, and Artificial Neural Network) learnings. The Support Vector Machine (SVM), Na Bayes (NB), Logistic Regression (LR), and Decision Tree (DT) algorithms within supervised learning, the K-Nearest Neighbors algorithm (KNN) within instance- based learning, the Random Forest (RF), Adaptive Boosting (Adaboost), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (GB) algorithms within ensemble learning. Also, one of the natural language processing algorithms, n-gram, is used to identify the cuneiform dialect. The best result belongs to an ensemble of Random Forest classifiers working on character-level features with a macro averaged F1 score of 96%, and the best outcome for the n-grams algorithm is 0.82% of di-gram.

Wine tasting notes as formulaic texts

The linguistic term “formulaic language” refers to fixed expressions or sequences of words that are often used as a single unit and are typically stored in memory and retrieved as complete units during communication, rather than being constructed or analyzed anew each time by the rules of grammar. Today, however, the concept of formulaicity has expanded beyond individual word combinations to include larger units as formulaic texts. In certain text genres such as abstracts and recipes, formulaicity is visible not only at the level of wording, but also in text content, structure, and layout. But it has not been examined in other text genres where professionals could benefit from this fixed structure in order to produce acceptable texts. In the present article we aim to analyze if wine tasting notes can be considered formulaic in nature. To meet our expectations, a corpus of wine tasting notes written originally in English will be used and collocations that help build up a text examined; by using Biel’s text-organizing patterns and term-embedding collocations, and by analyzing tasting notes in terms of Gülich’s criteria for formulaic texts, the present study will try to show if wine tasting notes are indeed formulaic. Our findings enhance dictionary entries by capturing the full spectrum of sensory experiences and professional terminology in oenology, improving lexical entries in both oenology and general monolingual dictionaries through the identification and categorization of formulaic language in wine tasting notes.

Comparative Examination of Spoken Language Recognition Through Deep Learning Algorithms – A Review

This work investigates the area of spoken language recognition, which is essential to language translation and natural language processing. It examines existing models that have been trained on deep learning techniques, datasets, and performance metrics. The article presents a thorough comparative examination of spoken language identification methods using deep learning, highlighting their advantages and disadvantages. In order to help researchers develop new language identification models for speech signals, the effectiveness of spoken language models is examined. Additionally, a number of spoken language identification models were investigated using a variety of deep learning approaches, datasets, and performance indicators. This analysis focused on the key traits and challenges that these models face.

Visibility for Indigenous Students and Their Languages: Analysis of Home Language Data in Federal Reports across Seven U.S. States

There is an increasing number of children in the U.S. classified as English Learners (ELs). Accurately identifying and supporting ELs in their academic settings entails understanding their non-English language experiences. This study presents findings from language reporting practices from seven U.S. states by examining how states account for the linguistic diversity of Indigenous Mesoamerican languages. Our findings reveal varied state approaches and underscore the limitations of current federal guidance, which limits the recognition of students’ non-English language experiences. We advocate for updating language identification practices and policies and propose a new framework for accurate language identification and continuous monitoring of student linguistic diversity.

A Hybrid Scene Text Script Identification Network for Regional Indian Languages

In this work, we introduce WAFFNet, an attention-centric feature fusion architecture tailored for word-level multi-lingual scene text script identification. Motivated by the limitations of traditional approaches that rely exclusively on feature-based methods or deep learning strategies, our approach amalgamates statistical and deep features to bridge the gap. At the core of WAFFNet, we utilized the merits of Local Binary Pattern—a prominent descriptor capturing low-level texture features with high-dimensional, semantically-rich convolutional features. This fusion is judiciously augmented by a spatial attention mechanism, ensuring targeted emphasis on semantically critical regions of the input image. To address the class imbalance problem in multi-class classification scenarios, we employed a weighted objective function. This not only regularizes the learning process but also addresses the class imbalance problem. The architectural integrity of WAFFNet is preserved through an end-to-end training paradigm, leveraging transfer learning to expedite convergence and optimize performance metrics. Considering the under-representation of regional Indian languages in current datasets, we meticulously curated IIITG-STLI2023, a comprehensive dataset encapsulating English alongside six under-represented Indian languages: Hindi, Kannada, Malayalam, Telugu, Bengali, and Manipuri. Rigorous evaluation of the IIITG-STLI2023, as well as the established MLe2e and SIW-13 datasets, underscores WAFFNet’s supremacy over both traditional feature-engineering approaches as well as state-of-the-art deep learning frameworks. Thus, the proposed WAFFNet framework offers a robust and effective solution for language identification in scene text images.

Improved Arithmetic Optimization Algorithm with Transfer Learning based Arabic Sign Language Identification System

The Arabic sign language (ArSL) has witnessed ground-breaking research activities to identify hand gestures and signs through the deep learning (DL) model. SL is a unique communication tool that bridges the gaps between people with hearing impairment and ordinary people. The ArSL recognition system is of immense importance for different groups of people since it enables individuals with hearing impairment to communicate effectively. In SLs, signs are characterized by discrepancies in hand positions, shapes, motions, body parts, and facial expressions, posing a crucial threat to visual recognition in computer vision (CV). An automated sign detection technique needs two primary courses of action: the recognition of specific features and the classification of the input dataset. Previously, several approaches for detecting and classifying SLs had been proposed. In this study, an Improved Metaheuristics with Transfer Learning based Arabic Sign Language Identification System (IMTL-ArSL) method is developed. The primary objective of the IMTL-ArSL method is to detect and classify the existence of various signs in the Arabic language. In the IMTL-ArSL model, the bilateral filtering (BF) model is initially used for preprocessing. Besides, the Residual Network (ResNet50v2) model is applied for extracting features, and an improved arithmetic optimization algorithm (IAOA) model is utilized for hyperparameter tuning. Finally, a gated recurrent unit (GRU) network is exploited to identify sign languages. The empirical analysis of the IMTL-ArSL approach is tested using a benchmark dataset. The experimental values of the IMTL-ArSL approach portrayed a superior accuracy value of 93.87% over other techniques.

Implementation of Sibi Sign Language Realtime Detection Program (Case Studi At Sekolah Luar Biasa Negeri 1 Tabanan)

Indonesian deaf people utilize SIBI to communicate using spoken words, gestures, facial expressions, and body language. SIBI, certified for Special Schools (SLB), helps deaf pupils communicate. This project implements SIBI (Indonesian Sign Language System) a real-time detection algorithm at Sekolah Luar Biasa Negeri 1 Tabanan using image processing and YoloV8 ultralytics deep learning. The program trains a sign language gesture detection model on Google Colab's GPU. The SIBI sign language images were used to train a YoloV8 object detection model. The camera captures movements, which the YoloV8 algorithm trained on SIBI gesture data processes. It can recognize gestures in real time and generate text to non-sign language users. The dataset has 107 class vocabulary and 7 class affix prefixes for complete gesture recognition. Shirt color, room brightness, and webcam quality affect detection rates. Optimal detection accuracy is 87.74% and subpar 58.02%. Despite these limitations, the strategy helps deaf students communicate more effectively with non-sign language speakers. This program improves inclusivity and communication in schools, making learning easier for hearing-impaired pupils. This work provides a reliable and quick sign language identification system to help deaf educators and caregivers with daily interactions and education.

OLF-ML: An Offensive Language Framework for Detection, Categorization, and Offense Target Identification Using Text Processing and Machine Learning Algorithms

The pervasiveness of offensive language on social media emphasizes the necessity of automated systems for identifying and categorizing content. To ensure a more secure online environment and improve communication, effective identification and categorization of this content is essential. However, existing research encounters challenges such as limited datasets and biased model performance, hindering progress in this domain. To address these challenges, this research presents a comprehensive framework that simplifies the utilization of support vector machines (SVM), random forest (RF) and artificial neural networks (ANN). The proposed methodology yields notable gains in offensive language detection, automatic categorization of offensiveness, and offense target identification tasks by utilizing the Offensive Language Identification Dataset (OLID). The simulation results indicate that SVM performs exceptionally well, exhibiting excellent accuracy scores (77%, 88%, and 68%), precision scores (76%, 87%, and 67%), F1 scores (57%, 88%, and 68%), and recall rates (45%, 88%, and 68%), proving to be practically successful in identifying and moderating offensive content on social media. By applying sophisticated preprocessing and meticulous hyperparameter tuning, our model outperforms some earlier research in detecting and categorizing offensive language tasks.

“What Is Your Primary Language?”: Spatial Considerations of Primary Language Identification in a Multilingual Rural Region

In small-scale multilingual societies, an individual uses a large number of languages and usually identifies one of them as the primary language. Although empirically observable, primary language has been significantly underexplored, particularly the spatiality of its identification. It is not clear whether and how place is related to why one language might be seen as being more significant than other languages. This study predicts the primary language used by multilingual individuals in an indigenous rural region of Cameroon based on two types of place: the residence of multilingual individuals and the birthplace of their family members. The research goals are supported by a random forest model using spatial-sociolinguistic data of the region. Results show that primary language can be predicted at a high accuracy. The residence of multilingual individuals and the birthplace of their family members capture the spatiality behind primary language identification. This work adds to the current discussion of the space–language connection and is valuable for maintaining the endangered languages in small-scale rural societies.

Whisper-SV: Adapting Whisper for low-data-resource speaker verification

Trained on 680,000 h of massive speech data, Whisper is a multitasking, multilingual speech foundation model demonstrating superior performance in automatic speech recognition, translation, and language identification. However, its applicability in speaker verification (SV) tasks remains unexplored, particularly in low-data-resource scenarios where labeled speaker data in specific domains are limited. To fill this gap, we propose a lightweight adaptor framework to boost SV with Whisper, namely Whisper-SV. Given that Whisper is not specifically optimized for SV tasks, we introduce a representation selection module to quantify the speaker-specific characteristics contained in each layer of Whisper and select the top-k layers with prominent discriminative speaker features. To aggregate pivotal speaker-related features while diminishing non-speaker redundancies across the selected top-k distinct layers of Whisper, we design a multi-layer aggregation module in Whisper-SV to integrate multi-layer representations into a singular, compacted representation for SV. In the multi-layer aggregation module, we employ convolutional layers with shortcut connections among different layers to refine speaker characteristics derived from multi-layer representations from Whisper. In addition, an attention aggregation layer is used to reduce non-speaker interference and amplify speaker-specific cues for SV tasks. Finally, a simple classification module is used for speaker classification. Experiments on VoxCeleb1, FFSVC, and IMSV datasets demonstrate that Whisper-SV achieves EER/minDCF of 2.22%/0.307, 6.14%/0.488, and 7.50%/0.582, respectively, showing superior performance in low-data-resource SV scenarios.

Language Identifier

This project presents a language identifier system focused on achieving high accuracy in identifying a specific set of 22 languages: Estonian, Swedish, English, Russian, Romanian, Persian, Pashto, Spanish, Hindi, Korean, Chinese, French, Portuguese, Indonesian, Urdu, Latin, Turkish, Japanese, Dutch, Tamil, Thai, and Arabic. Existing LI systems might struggle with the nuances of these languages, often prioritizing identification of more common languages. Our targeted approach allows for tailored optimization to achieve superior accuracy. We employ the Multinomial Naive Bayes (MNB) algorithm due to its effectiveness in text classification tasks and its ability to handle the high-dimensional, sparse features characteristic of LI based on character and word frequencies. The system achieves a promising accuracy of 95% using an 80/20 split for training and testing data. Keywords: Natural Language Processing (NLP), Language Identification (LI), Multinomial Naive Bayes (MNB), Text Classification, Language Barriers, Accuracy, Text Analysis.

Tables and families. Value, emotions and aesthetics of conviviality in two culinary manuscripts of the Mexican elites of the 19th century

ABSTRACT The attributes and cultural meanings of the table as a place for coexistence around food are sources of relevance for the study of the history and daily life of families from the past. The aim of this article is to analyze the social value given by families to the shared act of consuming food at elite 19th-century Mexican tables. This is done through the documentary study of two early and unpublished family culinary manuscripts, handwritten cookbooks created and in use in the period around the declaration of independence of Mexico in 1821. The identification of technical language and words that express affection in recipes, as well as table-mounting related sections in these manuscripts, allows for the assessment of females’ practice or male’s intention of transmitting culinary knowledge orally and in writing. The importance of seasoning as a cultural identity trait is also evident, as well as the weight given by families to affection and aesthetics when transforming, serving, and sharing food in both intimate and public tables. The capacity of Mexican consumption spaces and their associated practices to communicate, maintain or increase prestige is observed as well. These results could be comparable with other environments and historical contexts with similar characteristics and deserve further study.

Speaker-based language identification for Ethio-Semitic languages using CRNN and hybrid features

ABSTRACT Natural language is frequently employed for information exchange between humans and computers in modern digital environments. Natural Language Processing (NLP) is a basic requirement for technological advancement in the field of speech recognition. For additional NLP activities like speech-to-text translation, speech-to-speech translation, speaker recognition, and speech information retrieval, language identification (LID) is a prerequisite. In this paper, we developed a Language Identification (LID) model for Ethio-Semitic languages. We used a hybrid approach (a convolutional recurrent neural network (CRNN)), in addition to a mixed (Mel frequency cepstral coefficient (MFCC) and mel-spectrogram) approach, to build our LID model. The study focused on four Ethio-Semitic languages: Amharic, Ge’ez, Guragigna, and Tigrinya. By using data augmentation for the selected languages, we were able to expand our original dataset of 8 h of audio data to 24 h and 40 min. The proposed selected features, when evaluated, achieved an average performance accuracy of 98.1%, 98.6%, and 99.9% for testing, validation, and training, respectively. The results show that the CRNN model with (Mel-Spectrogram + MFCC) combination feature achieved the best results when compared to other existing models.