Hindi Dialects Research Articles

Sentiment analysis of Hinglish reviews using hybrid approaches

People are using social media to communicate their views and opinions on several topics, and it is becoming more popular as a medium of communication. Nowadays people are curious to hear about a film review before they watch it for a few days. A summary of all reviews for a film can help users make their decision by not wasting time reading all reviews. Several websites can have summery because of the rise of social media.As a result, the words sentiment analysis and text analysis developed their paths to becoming important computational linguistics and text analysis components. Opinions are articulated in India in recent years, using multi-lingual terms. In the field of sentiment analysis, this has become a new challenge. Machine learning methods, such as neural networks, have proven effective in this task; however, there is space for advancing to networks with greater accuracy.In this investigation,U. S. English, Hindi dialects, and datasets like Twitter sentiment corpus, Amazon Product Reviews, IMDB Movie reviews, and Several public opinion datasets have been used in this study. The proposed network may achieve the greatest known accuracy of 92.25 percent. As an outcome, the success of the suggested network can be duplicated in various fields.

Fijian school students’ Fiji English speech sound acquisition

Purpose: The aim of this study was to investigate Fijian students’ acquisition of Fiji English speech sounds. Method: Participants were 72 multilingual students (5–10 years) living in Fiji who spoke the Fijian or Fiji Hindi dialects of Fiji English. The students’ productions of single words from the Diagnostic Evaluation of Articulation and Phonology (DEAP) were analysed according to dialect using the Children’s Independent and Relational Phonological Analysis (CHIRPA). Result: Most English consonants were acquired by 6;0. English consonants that were not in the inventories of Fijian or Fiji Hindi were the last acquired, while those that were common between English and the two languages were some of the earlier acquired consonants. Percentage of consonants correct (PCC) for Fiji English was significantly lower for Grade 1 students (M = 93.01, SD = 0.07) in comparison to Grade 4 students (M = 99.03, SD = 0.01). Plosive and nasal sounds were acquired by 90% of the students earlier than fricative, approximant and affricate sounds, though acquisition of some plosive and fricative sounds varied between Fiji English dialects. All word-initial consonant clusters were acquired by 90% of the students by 9;6, with r-clusters tending to be the latest acquired. The acquisition of word-final consonant clusters was highly variable as students often produced appropriate dialectal variants. Conclusion: Multilingual Fijian children may acquire English specific sounds later than their monolingual English-speaking peers. Consideration of the language environment and analysis of the speech sample with reference to the child’s dominant English dialect is imperative for valid identification of speech sound disorder (SSD) in children who speak different dialects of English.

Linguistic Identity with Special Reference to Western Hindi Dialects

Linguistic identity is the common bond that people share when they can understand each other in their native tongues, even if they share no other common heritage. Linguistic identity gets trickier when you’re talking about two people who may share linguistic bonds but come from mutually hostile ethnic groups. With racial and ethnic identity, linguistic identity does not exist in isolation; it is frequently yet one more facet of how a person identifies. There’s what we might call “reverse linguistic identity.” As Boas demonstrated over a century ago, everyone has at least three independent identities: race (in the traditional, not the anthropological sense), culture, and language. Language (or linguistic) identity take to mean the speech community with which someone is identified. This is probably always a historical phenomenon, either of birth or of personal choice. Most subjects to personal choice are culture and language, for instance, a given person identifies with, or belongs to a particular culture, and speaks a particular language. These identities may be due to birth or socialization, or they may be the result of a deliberate choice NOT to identify with the language and culture of birth. Linguistic identities are double-edged swords because, while functioning in a positive and productive way to give people a sense of belonging, they do so by defining an “us” in opposition to a “them” that becomes all too easy to demonize. All identity markers of a social group together constitute the “culture” or cultural identity of the social group. Therefore, the loss of one marker does not automatically entails the loss of cultural identity. Given the rich multilingual tradition of India where languages act as facilitators rather than as barriers in communication, one hopes that as linguistic identity. This paper is a case study of the author’s inferences regarding the Western Hindi dialects analysis.

Shaping modern Indo-Aryan isoglosses

Abstract Since the pioneering paper by Emenau (1956) there have been many attempts (cf. Masica 1976, 2001; Ebert 2001; among many others) to select areal features which are shared among languages spoken in South Asia. However, there has been little consent on the number of such features and the possible direction of their spread. In this paper we are focusing on two selected isoglosses, namely alignment and constituent order. Both of them have been used to define the Indo-Aryan linguistic area: alignment is one of the key elements to distinguish western from eastern Indo-Aryan (Peterson 2017) and word order is one of the innovations which differentiates some of the “Outer” languages from “Inner” Indo-Aryan languages (Zoller 2017: 15). This article focuses on two languages which are said to determine these isoglosses: Awadhi and Kashmiri. Our study of Awadhi shows that the isogloss delineating ergative or accusative case marking zones is situated in the area where the so-called Eastern Hindi dialects (among them Awadhi) are spoken. As we will demonstrate, this specific isogloss is substantially supported by diachronic evidence. The second language under consideration, namely Kashmiri, is an example of an “Outer” language with a quite stable V2 feature. Both Awadhi and Kashmiri are compared with Pahari, a language branch which functions as a link between the two of them. Our comparison of Kashmiri with certain Western Pahari Himachali languages shows that there is no clear borderline between two language groups supported by word order. We conclude from these case studies that the study of isoglosses is by definition a study of fluid boundaries, and qualitative, historical studies of one language can prove or disprove hypotheses based on synchronic similarities between languages.

Analysis and modeling of acoustic information for automatic dialect classification

A primary challenge in the field of automatic speech recognition is to understand and create acoustic models to represent individual differences in their spoken language. Individual’s age, gender; their speaking styles influenced by their dialect may be few of the reasons for these differences. This work investigates the dialectal differences by measuring the analysis of variance of acoustic features such as, formant frequencies, pitch, pitch slope, duration and intensity for vowel sounds. This paper attempts to discuss methods to capture dialect specific knowledge through vocal tract and prosody information extracted from speech that can be utilized for automatic identification of dialects. Kernel based support vector machine is utilized for measuring the dialect discriminating ability of acoustic features. For the spectral feature shifted delta cepstral coefficients along with Mel frequency cepstral coefficients gives a recognition performance of 66.97 %. Combination of prosodic features performs better with a classification score of 74 %. The model is further evaluated for the combination of spectral and prosodic feature set and achieves a classification accuracy of 88.77 %. The proposed model is compared with the human perception of dialects. The overall work is based on four dialects of Hindi; one of the world’s major languages.

Fusion of multi-stream speech features for dialect classification

Current research in the area of voice recognition has entered a new stage. It does not only concentrate on the correct evaluation of linguistic information embodied in the speech signal, it also works towards identification of variations naturally present in speech. Undoubtedly, the focus is to enhance the accuracy and precision of the developed technique. Speaker’s accent due to his native dialect is one of the major source of variability. Prior knowledge of the spoken dialect will help in the creation of multi-model speech recognition system and can enhance its recognition performance. This paper focusses on applying some of the established dialect identification techniques to identify speaker’s spoken dialect among dialects of Hindi. Fusion of multiple streams obtained as a combination of phonetic and prosodic features is implemented to exploit the acoustic information. The work presented here also exploits the ability of AANN to capture the distribution of data points in a reduced number and further to classify them into groups. System performance for different level of fusion is recorded for Hindi dialect classification. It is observed that Duration as prosodic feature is an important cue for automatic dialect identification systems.

Spectral and prosodic features-based speech pattern classification

Speech pattern produced by individuals are unique. This uniqueness is due to the accent influenced by individual's native dialect. Prior knowledge of spoken dialect provides valuable information for speaker profiling and incorporating them in the decision parameter can improve the system performance. In this paper, an auto-associative neural network model has been proposed to model intrinsic characteristics of speech features for dialect classification. This paper highlights the sufficiency of few spectral and prosodic features for identification of Hindi dialects. Experimental results show that system performance is the best when both spectral and prosodic features are combined to use as input. In the presence of noise, performance of a conventional ASR starts to degrade. The NOISEX-92 database is used to add white noise to the recorded utterances in the range of 0 dB to 20 dB. This paper evaluates the dialect classification system's performance for SNRs in this range.

Acoustic-Phonetic Feature Based Dialect Identification in Hindi Speech

Abstract Every individual has some unique speaking style and this variation influences their speech characteristics. Speakers’ native dialect is one of the major factors influencing their speech characteristics that influence the performance of automatic speech recognition system (ASR). In this paper, we describe a method to identify Hindi dialects and examine the contribution of different acoustic-phonetic features for the purpose. Mel frequency cepstral coefficients (MFCC), Perceptual linear prediction coefficients (PLP) and PLP derived from Mel-scale filter bank (MF- PLP) have been extracted as spectral features from the spoken utterances. They are further used to measure the capability of Auto-associative neural networks (AANN) for capturing non-linear relation specific to information from spectral features. Prosodic features are for capturing long - range features. Based on these features efficiency of AANN is measured to model intrinsic characteristics of speech features due to dialects.

Hindi, Hinglish: Head to Head

Hindi, Hinglish: Head to Head

100 Years of Presence or Absence?

The cinema of India is considered synonymous with Bollywood. This misnomer actually hides in itself myriad hues from the hinterland. While some regions have developed competitive industries, some others are dying a slow death due to state apathy. The situation is particularly bad in regions where Hindi dialects are spoken, namely Uttar Pradesh, Rajasthan, Punjab and Haryana. While Bhojpuri films and Punjabi films have started achieving wide commercial success, the identity of Haryanvi cinema has gotten lost somewhere. If Haryanvi is just another dialect of Hindi, then why has Hindi cinema not been able to fulfil the cultural needs of the Haryanvi speaking audience? Is there scope for investigating the larger socio-economic factors that have reduced Haryanvi cinema to this state? This article is an attempt to investigate the political economy of Haryanvi cinema in relation to the overarching presence of the ubiquitous big brother ‘Bombay cinema’.

Shellac, Bakelite, Vinyl, and Paper: Artifacts and Representations of North Indian Art Music

Short songs in dialects of Hindi are the basis for improvisation in all the genres of North Indian classical vocal music. These songs, bandises, constitute a central pillar of North Indian culture, spreading well beyond the geographic frontiers of Hindi itself. Songs are significant as being the only aspect of North Indian music that is fixed and handed down via oral tradition relatively intact. They are regarded as the core of Indian art music because they encapsulate the melodic structures upon which improvisation is based. In this paper we aim to look at some issues raised by the idiosyncrasies of written representations of songs as they has occurred within the Indian cultural milieu, and then at issues that have emerged in the course of our own ongoing efforts to represent khyal songs from the perspectives of somewhat insidish outsiders.

Maithili in the Indian census

Abstract The role played by agencies like the census has been important in deciding upon language issues in India (Brass, 1974; Khub‐chandani, 1983; Mahapatra, 1990). In the fluid linguistic zone of north India, many languages are taken to be dialects of Hindi. The official status of a speech variety as a language or a dialect is largely dependent on the categorisation made by the Indian census. Often Maithili is referred to as Hindi in different censuses. Here I have tried to give a sociolinguistic appraisal of the representation of languages in the Indian census with special reference to the status of Maithili, which deals with the problems that arise out of the inconsistent approach of the census towards ‘language’, ‘dialect’ and ‘mother tongue’. The speakers of Maithili and other such languages face problems of identity and loyalty. On the one hand, their concern to maintain Maithili as an independent language is seen as a hindrance to the homogeneity of the ‘Hindi belt’ and, on the other, there is an...

Malaysian Drama in English: Is there a Case for a Post-Mortem?

Malaysian drama in English (MDE) is an inelegant name, but it describes exactly a curious breed of theatre in Malaysia: Englishlanguage drama, which seeks to be locally appropriate, in a country whose polycultural history has resulted in the presence of a diverse mixture of languages including Malay (the National Language), the major dialects of Chinese, Hindi, Tamil and other Indian languages, as well as English. Malaysian drama in English has existed for some twentyfive years – not a long history, even measured by the standards of the New Literatures of post-colonial nations. Its quarter century of life has been short and turbulent. MDE has followed a course marked by race riots, language demonstrations, defections from its fold, institutional indifference, censorship, and the gradual withering of the English language itself as a medium viable within the national context. Much of the history of MDE has been affected by the major socio-political changes of the nation. It is not possible, then, to see MDE as an autonomous, selfenclosed entity. Its life cannot be extricated from the national history out of which it grows, and its story is inseparable from the political fortunes of the English language in Malaysia.

The Rise of Standard Hindi and Early Hindi Prose Fiction

Pre-nineteenth-century prose and prose fiction in Hindi dialects. It is well known that the use of Sanskritized prose in Hindi dialects dates from long before the beginning of the 19th century A.D. The pre-19th-century prose texts which have been preserved in Braj Bhāṣā, Khaṛi Bolī, and Rājasthānī dialects have a collective importance for our subject as antecedents of the Sanskritized style of standard Hindi, based on Khaṛī Bolī, which emerged in the 19th century. Their existence demonstrates that before this time there were already recognized traditions of prose-writing in the main western Hindi dialects, and that within these traditions it was customary to use Sanskrit words to supplement the vocabulary of one's dialect, and to work in the Devanāgarī script.

Some Bhartṛhari commentaries in early braj Bhāṣā prose

Since the beginning of the twentieth century evidence has become increasingly available to show that the use of Sanskritized prose in certain Hindi dialects long antedates the spread of Western influences in India. We owe this evidence above all to the efforts of the Nāgarī Pracāriṇī Sabhā of Banāras, which from the year 1900 has instituted an annual search for Hindi manuscripts, and has published reports of its investigations.

Phonological Differences in Three Hindi Dialects

There has been a great deal of interest latelyl in the application of structural techniques to problems of dialect comparison.2 Studies in this field are rendered difficult, however, by the fact that many of the existing surveys were planned at a time when the phonemic principle was still insufficiently understood and questionnaires lacked examples illustrating points which were later found to be of great importance. As a result, many of the field records available at present are inadequate for complete phonemic analysis.3 If a geographical study is to yield enough structurally relevant information, it must be based on advance knowledge of the major phonemic and phonetic features characteristic of the region. A structural linguistic survey in an area not previously studied might therefore consist of two stages: (a) a preliminary series of phonological studies at selected points in the area, using informant methods of the type employed in studies of a single dialect, with a classification of dialect differences found; and (b) a detailed geographical survey with the aid of questionnaires to determine the spread of the features discovered in the preliminary study. The present paper concerns the first of these stages. It consists of two parts. The first deals with the phonology of three neighboring village dialects of Western Hindi; the second undertakes a classification of differences among the three dialects which might furnish criteria for selecting items for a geographical questionnaire. The studies were made in Rankhandi Village, (Tahsil [subdistrict] Deoband in Saharanpur District, United Provinces), Sivaya (Tahsil Ghaziabad, Merut District, U.P.), and Kalraun (Karnal District, East Punjab); see the map at