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About 10,000 papers for “Language identification”

Language Identification

Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Guha Jayachandran · SpringerBriefs in computer science · 2021

Cited by 2

AUTOMATIC LANGUAGE IDENTIFICATION

2008

Automatic language identification is the process by which the language of a digitized speech utterance is recognized by a computer. In this paper, we will describe the set of available cues for language identification and discuss the different approaches to building working systems. This overview includes a range of historic approaches, con-temporary systems that have been evaluated on standard databases, as well as promising future approaches. Com-parative results are also reported. 1.

Cited by 0

Automatic Language Identification

Nejla Qafmolla · European Journal of Language and Literature · 2017

Automatic Language Identification (LID) is the process of automatically identifying the language of spoken utterance or written material. LID has received much attention due to its application to major areas of research and long-aspired dreams in computational sciences, namely Machine Translation (MT), Speech Recognition (SR) and Data Mining (DM). A considerable increase in the amount of and access to data provided not only by experts but also by users all over the Internet has resulted into both the development of different approaches in the area of LID – so as to generate more efficient systems – as well as major challenges that are still in the eye of the storm of this field. Despite the fact that the current approaches have accomplished considerable success, future research concerning some issues remains on the table. The aim of this paper shall not be to describe the historic background of this field of studies, but rather to provide an overview of the current state of LID systems, as well as to classify the approaches developed to accomplish them. LID systems have advanced and are continuously evolving. Some of the issues that need special attention and improvement are semantics, the identification of various dialects and varieties of a language, identification of spelling errors, data retrieval, multilingual documents, MT and speech-to-speech translation. Methods applied to date have been good from a technical point of view, but not from a semantic one.

Cited by 3Open access

QURATOR Language Identification

DFKI · European Language Grid · 2022

Service for the identification of the language of a piece of text.

Cited by 0Open access

Sign Language Identification

Mrs. Dr. V. Suganthi, C. Thavapriya, T. Mirudhu Bashini · International Journal of Research Publication and Reviews · 2024

Sign language identification is a computer vision technique used to identify and track human body movements and positions. It involves analyzing images or videos to locate key body parts, such as the head, torso, arms, and legs, and estimating their spatial relationships.Sign language identification has a wide range of applications, including f itness tracking, sports analysis, gaming, virtual reality, and surveillance. This abstract provides an overview of the concept of pose detection and its potential uses.

Cited by 1Open access

TRAP Language Identification

Kyu J. Han, Sriram Ganapathy, Ming Li et al. · 2016

Automatic language identification or detection of au-dio data has become an important preprocessing step for speech/speaker recognition and audio data mining. In many surveillance applications, language detection has to be per-formed on highly degraded audio inputs. In this paper, we present our work on language detection in highly degraded ra-dio channel scenarios. We provide a brief description of the Targeted Robust Audio Processing (TRAP) language detection system built for the Phase II Evaluation of the Robust Automatic Transcription of Speech (RATS) program. This system is a combination of 15 systems with different frontends and speech activity decisions. We also analyze the usefulness of multi-layer perceptron (MLP) based non-linear projection of i-vectors be-fore SVM classification. The proposed backend reduces the Equal Error Rate (EER) by 11%–25 % relative compared to the baseline PCA-based feature representation for SVM classifica-tion, on the RATS test data consisting of data from eight high-frequency radio communication channels. Index Terms: Language identification (detection), highly de-graded radio channel, RATS, i-vector, multi-layer perceptron.

Cited by 0

Automatic language identification

K. P. Li, T. J. Edwards · The Journal of the Acoustical Society of America · 1979

A study on automatic language identification has recently been completed. The study included 200 talkers from ten different languages. Data from five languages were comprised of reading data from all male talkers while the other five languages were comprised of two-way conversational data from both male and female talkers. All data were band-limited to 350-3000 Hz and digitized at 8 kHz. Using an automatic segmentation program developed at TRW [J. Acoust. Soc. Am. Suppl. 1 64, S179(A) (1978)] which provided a set of six acoustic-phonetic labels, several forms of Markov types of models and classification methods were developed to identify each language independently of the talker and context. Half of the talkers were used for training the models, while the other half was used for independent test. Classification methods, statistics of the data, and identification results will be presented.

Cited by 3Open access

Native language identification

Scott Jarvis, Magali Paquot · Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) · 2015

The main objective of this chapter is to examine the automatic techniques that can be applied to learner corpora to identify learners’ mother tongue backgrounds from their patterns of production in the target language. The chapter focuses particularly on aspects of learner corpus design that have a direct bearing on the results (size, topic homogeneity, type and level of annotation). Special attention is also paid to the contribution of NLI research to SLA, more particularly in the form of the detection-based approach to transfer. NLI is a relatively new line of enquiry: research so far has focused on identifying the native language of writers using English as a foreign language and the chapter will thus deal with English as a target language.

Cited by 4Open access

LVCSR-based language identification

Tanja Schultz, Ivica Rogina, Alex Waibel · KITopen · 2002

Automatic language identification is an important problem in building multilingual speech recognition and understanding systems. Building a language identification module for four languages we studied the influence of applying different levels of knowledge sources on a large vocabulary continuous speech recognition (LVCSR) approach, i.e. phonetic, phonotactic, lexical, and syntactic-semantic knowledge. The resulting language identification (LID) module can identify spontaneous speech input and can be used as a front end for the multilingual speech-to-speech translation system JANUS-II. A comparison of five LID systems showed that the incorporation of lexical and linguistic knowledge reduces the language identification error for the 2-language tests up to 50%. Based on these results we build a LID module for German, English, Spanish, and Japanese which yields 84% identification rate on the spontaneous scheduling task (SST).

Cited by 47Open access

Statistical Identification of Language

Ted Dunning · 1994

A statistically based program has been written which learns to distinguish between languages. The amount of training text that such a program needs is surprisingly small, and the amount of text needed to make an identification is also quite small. The program incorporates no linguistic presuppositions other than the assumption that text can be encoded as a string of bytes. Such a program can be used to determine which language small bits of text are in. It also shows a potential for what might be called `statistical philology' in that it may be applied directly to phonetic transcriptions to help elucidate family trees among language dialects. A variant of this program has been shown to be useful as a quality control in biochemistry. In this application, genetic sequences are assumed to be expressions in a language peculiar to the organism from which the sequence is taken. Thus language identification becomes species identification.

Cited by 0

Spoken Indian Language Identification

Bakshi, Aarti, Kopparapu, Sunil Kumar · IEEE DataPort · 2020

Spoken Indian Language Identifcation (9 languages, 8 different utterance lengths)

Cited by 0Open access

Multilingual native language identification

Shervin Malmasi, Mark Dras · Natural Language Engineering · 2015

Abstract We present the first comprehensive study of Native Language Identification (NLI) applied to text written in languages other than English, using data from six languages. NLI is the task of predicting an author’s first language using only their writings in a second language, with applications in Second Language Acquisition and forensic linguistics. Most research to date has focused on English but there is a need to apply NLI to other languages, not only to gauge its applicability but also to aid in teaching research for other emerging languages. With this goal, we identify six typologically very different sources of non-English second language data and conduct six experiments using a set of commonly used features. Our first two experiments evaluate our features and corpora, showing that the features perform well and at similar rates across languages. The third experiment compares non-native and native control data, showing that they can be discerned with 95 per cent accuracy. Our fourth experiment provides a cross-linguistic assessment of how the degree of syntactic data encoded in part-of-speech tags affects their efficiency as classification features, finding that most differences between first language groups lie in the ordering of the most basic word categories. We also tackle two questions that have not previously been addressed for NLI. Other work in NLI has shown that ensembles of classifiers over feature types work well and in our final experiment we use such an oracle classifier to derive an upper limit for classification accuracy with our feature set. We also present an analysis examining feature diversity, aiming to estimate the degree of overlap and complementarity between our chosen features employing an association measure for binary data. Finally, we conclude with a general discussion and outline directions for future work.

Cited by 48Open access

Geographically-Informed Language Identification

Jonathan Dunn, Lane Edwards-Brown · arXiv (Cornell University) · 2024

This paper develops an approach to language identification in which the set of languages considered by the model depends on the geographic origin of the text in question. Given that many digital corpora can be geo-referenced at the country level, this paper formulates 16 region-specific models, each of which contains the languages expected to appear in countries within that region. These regional models also each include 31 widely-spoken international languages in order to ensure coverage of these linguae francae regardless of location. An upstream evaluation using traditional language identification testing data shows an improvement in f-score ranging from 1.7 points (Southeast Asia) to as much as 10.4 points (North Africa). A downstream evaluation on social media data shows that this improved performance has a significant impact on the language labels which are applied to large real-world corpora. The result is a highly-accurate model that covers 916 languages at a sample size of 50 characters, the performance improved by incorporating geographic information into the model.

Cited by 1Open access

Chinese Native Language Identification

Shervin Malmasi, Mark Dras · 2014

We present the first application of Native Language Identification (NLI) to nonEnglish data. Motivated by theories of language transfer, NLI is the task of identifying a writer’s native language (L1) based on their writings in a second language (the L2). An NLI system was applied to Chinese learner texts using topicindependent syntactic models to assess their accuracy. We find that models using part-of-speech tags, context-free grammar production rules and function words are highly effective, achieving a maximum accuracy of 71% . Interestingly, we also find that when applied to equivalent English data, the model performance is almost identical. This finding suggests a systematic pattern of cross-linguistic transfer may exist, where the degree of transfer is independent of the L1 and L2.

Cited by 36Open access

Automatic Native Language Identification

Ilia Markov · 2018

Tesis (Doctorado en Ciencias de la Computacion), Instituto Politecnico Nacional, CIC, 2018, 1 archivo PDF, (110 paginas). tesis.ipn.mx

Cited by 2Open access

HENSOLDT ANALYTICS Language Identification

Hensoldt Analytics · European Language Grid · 2022

HENSOLDT ANALYTICS MediaMiningIndexer LID - Language Identification engine that provides classification of clear text into following languages: Arabic, Bulgarian, Catalan, Czech, Danish, German, Greek, English, Spanish, Farsi, French, Hebrew, Croatian, Hungarian, Bahasa Indonesia, Italian, Malay, Dutch, Norwegian, Polish, Pashto, Portugese, Romanian, Russian, Slovak, Tosk Albanian, Swedish, Swaheli, Turkish, Urdu, Chinese Simplified.

Cited by 0Open access