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Human beings have their own speaking style which helped them in depicting their native language. The major reason behind variability in some language is due to varying dialect of the speakers. In the field of Automatic Speech Recognition (ASR), key challenge is to recognize and to generate an acoustic model which represents differences of redundant acoustic features. In this paper, an issue of dialect classification is perform on the basis of tonal aspects of laryngeal phoneme [h]. This is an empirical study of [h] sound words in four major dialects of Indian Punjabi language with two key parameters, namely F0 variation, and acoustic space, which are calculated using two formant frequencies: F1, and F2. The results are based on four different dialects which provide us some interesting hypotheses and are explored with self-created dataset. The speech analysis tool PRAAT features have been extracted and correlations are studied using Statistical Package for the Social Sciences (SPSS). Each variable has been compared with same variable of all other dialects. The results analysis showed that the fundamental frequency of these vowels are influenced distinctly in different dialectal conditions. Apart F1 and F2 have shown a significant correlation with each spoken dialect. Further work is extended through processing of acoustic information at feature level or by comparing the performance analysis using basic or hybrid Linear Predictive Cepstral Coefficients feature extraction methods. The result shows that the hybrid LPCC + F0 system achieved a Relative Improvement (R.I.) of 6.94% on Subspace Gaussian Mixture Model model in comparison to that of basic LPCC approach respectively.

Background Stigma is a common experience for people living with dementia, is associated with many negative consequences including discrimination and social exclusion, and can serve as a barrier to accessing services for people living with dementia. Unfortunately, dementia‐related stigma may be even more likely to occur in ethno‐racial communities. Dementia resources that are culturally relevant to ethno‐racial communities and written in the languages read in these communities are lacking. Forward With Dementia (FWD) is an initiative that aims to combat stigma and raise awareness about living well with dementia. To begin to address the gap in culturally relevant information and resources, the Canadian FWD team co‐designed resources that were adapted for the Chinese, South Asian and Italian communities. Method Co‐design teams were established for each ethno‐racial community. Teams included family/friend care partners and health and social care providers. Unfortunately, we were unable to recruit people living with dementia to participate, likely due to increased dementia‐related stigma and the tendency of later diagnosis in these communities. Content from the existing FWD site served as the basis for the resources. We met monthly with each co‐design team to adapt and review the resources. Pictures, symbols, personal stories, and examples relevant to each community were used in the design of the resources. Once finalized, the resources were translated into relevant languages. Result Over a 1‐year period, a total of 187 newly adapted resources were created. This included 13 core resources and 1‐2 stories by care partners for each community. Chinese resources were translated into Simplified and Traditional Chinese, South Asian resources were translated into Punjabi, Hindi and Urdu, and Italian resources were translated into Italian. The resources are also available in English and French. During a 3‐month marketing campaign to promote the resources, over 157,000 individuals were reached with 4300+ views of the resources and 1400+ downloads. Conclusion The new culturally‐adapted and translated FWD resources address a significant gap in support for people with dementia and care partners from ethno‐racial communities.

Nowadays we see huge amount of information is available on both, online and offline sources. For single topic we see hundreds of articles are available, containing vast amount of information about it. It is really a difficult task to manually extract the useful information from them. To solve this problem, automatic text summarization systems are developed. Text summarization is a process of extracting useful information from large documents and compressing them into short summary preserving all important content. This survey paper hand out a broad overview on the work done in the field of automatic text summarization in different languages using various text summarization approaches. The focal centre of this survey paper is to present the research done on text summarization on Indian languages such as, Hindi, Punjabi, Bengali, Malayalam, Kannada, Tamil, Marathi, Assamese, Konkani, Nepali, Odia, Sanskrit, Sindhi, Telugu and Gujarati and foreign languages such as Arabic, Chinese, Greek, Persian, Turkish, Spanish, Czeh, Rome, Urdu, Indonesia Bhasha and many more. This paper provides the knowledge and useful support to the beginner scientists in this research area by giving a concise view on various feature extraction methods and classification techniques required for different types of text summarization approaches applied on both Indian and non-Indian languages.

India is the land of language diversity with 22 major languages having more than 720 dialects, written in 13 different scripts. Out of 22, Hindi, Bengali, Punjabi is ranked 3rd, 7th and 10th most spoken languages around the globe. Expect Hindi, where one can find some significant research going on, other two major languages and other Indian languages have not fully developed Automatic Speech Recognition systems. The main aim of this paper is to provide a systematic survey of the existing literature related to automatic speech recognition (i.e. speech to text) for Indian languages. The survey analyses the possible opportunities, challenges, techniques, methods and to locate, appraise and synthesize the evidence from studies to provide empirical answers to the scientific questions. The survey was conducted based on the relevant research articles published from 2000 to 2018. The purpose of this systematic survey is to sum up the best available research on automatic speech recognition of Indian languages that is done by synthesizing the results of several studies.

This study was conducted with the aim of exploring the general parsing mechanisms involved in processing different kinds of dependency relations, namely verb agreement with subjects versus objects in Punjabi, an SOV Indo-Aryan language. Event related brain potentials (ERPs) were recorded as twenty-five native Punjabi speakers read transitive sentences. Critical stimuli were either fully acceptable as regards verb agreement, or alternatively violated gender agreement with the subject or object. A linear mixed-models analysis confirmed a P600 effect at the position of the verb for all violations, regardless of whether subject or object agreement was violated. These results thus suggest that an identical mechanism is involved in gender agreement computation in Punjabi regardless of whether the agreement is with the subject or the object argument.

Speech recognition has been an active field of research in the last few decades since it facilitates better human–computer interaction. Native language automatic speech recognition (ASR) systems are still underdeveloped. Punjabi ASR systems are in their infancy stage because most research has been conducted only on adult speech systems; however, less work has been performed on Punjabi children’s ASR systems. This research aimed to build a prosodic feature-based automatic children speech recognition system using discriminative modeling techniques. The corpus of Punjabi children’s speech has various runtime challenges, such as acoustic variations with varying speakers’ ages. Efforts were made to implement out-domain data augmentation to overcome such issues using Tacotron-based text to a speech synthesizer. The prosodic features were extracted from Punjabi children’s speech corpus, then particular prosodic features were coupled with Mel Frequency Cepstral Coefficient (MFCC) features before being submitted to an ASR framework. The system modeling process investigated various approaches, which included Maximum Mutual Information (MMI), Boosted Maximum Mutual Information (bMMI), and feature-based Maximum Mutual Information (fMMI). The out-domain data augmentation was performed to enhance the corpus. After that, prosodic features were also extracted from the extended corpus, and experiments were conducted on both individual and integrated prosodic-based acoustic features. It was observed that the fMMI technique exhibited 20% to 25% relative improvement in word error rate compared with MMI and bMMI techniques. Further, it was enhanced using an augmented dataset and hybrid front-end features (MFCC + POV + Fo + Voice quality) with a relative improvement of 13% compared with the earlier baseline system.

Automatic Speech Recognition (ASR) system for children is as important as for adults since children are more dependent on these systems nowadays, such as computer games, reading tutors, foreign language learning tools, etc. Consequently, this article aims to present several important aspects related to children's speech recognition systems, in which a comprehensive review is presented. Acoustic and linguistic challenges of children's speech are presented thoroughly to understand the basic anatomy of children's articulation organs. A variety of challenges exist for the development of children's ASR, such as the collection of children's speech data is a very complex task; the available child corpora are not publicly accessible, children's speakers differ greatly due to linguistic and acoustic variations, and ASRs developed for one age group are not suitable for another age group. All these challenges are systematically described in this article. Various data augmentation methods are also explored here, along with different approaches to develop ASR in children's speech. It has been observed that the inaccessibility of child corpora publicly is a significant barrier to children's ASR. Apart from the challenges mentioned earlier related to children’s ASR, an attempt has been made to thoroughly review the children’s ASR in the case of Punjabi language, as this language is ranked 10th most spoken globally and is still considered a low-resource language. Further, various approaches for the development of children’s ASR such as traditional, hybrid and end-to-end (E2E) networks are also reported. In addition, an analytical summary and discussion are included. Graphical Abstract

This study explores the experiences of Punjabi (i.e., from the Punjab region in India) international undergraduate students (hereafter PS) attending Canadian higher education through a case study of a teaching university in British Columbia. The primary focus is on unpacking how PS’ experiences were underlined by labor mobility, immigration policies, and the marketization of international higher education. To recruit international students, many lower-tier Canadian universities apply a business model that relies heavily on agents. The outcome is that educational considerations are not central to admission and retention processes. The findings critique the Canadian education-migration model by identifying the complicity of Canadian higher education in lower-skill immigration and the negative educational and professional outcomes for PS that result from this complicity. The study highlights PS’ voices and experiences that can go overlooked in the context of market-driven higher education.

The performance of Automatic Speech Recognition (ASR) is directly proportional to the quality of the corpus used and the training data quantity. Data scarcity and more children’s speech variability degrades the performance of ASR systems. As Punjabi is a tonal language and low resource language, less data is available for Punjabi children’s speech. It leads to poor ASR performance for Punjabi children speech recognition. To overcome limited data conditions, in this paper, two corpora of different domains are evaluated for testing the feasibility of ASR performance. We have implemented Tacotron as an artificial speech synthesis system for Punjabi Language. The speech audios synthesized by Tacotron are merged with available speech corpus and tested on Punjabi children ASR using Mel Frequency Cepstral Coefficients (MFCC) + pitch feature extraction, and Deep Neural Network (DNN) acoustic modeling. It is noticed that the merged data corpus has shown reduced Word Error Rate (WER) of the ASR system with a Relative Improvement (RI) of 9-12%.

Indigenous language activism on social media is crucial in promoting and preserving linguistic and cultural diversity in today’s digital world. This study investigates Setswana and Punjabi language activism on the social media platform Facebook, focusing on how much community activists utilise this outlet for their language activism. This research investigates the strategies employed by activists and examines the unique challenges and opportunities they encounter. The study utilises qualitative content analysis to gather data from Facebook, covering the period from March 2022 to March 2023. Themes were developed using the Nvivo tool to analyse the collected data. The findings indicate that Setswana and Punjabi activists have made extensive use of Facebook for language promotion, employing various strategies in their activism during this time. Punjabi activists focus primarily on policy advocacy and community engagement, while Setswana activists highlight education and cultural heritage. Both groups of language activists exhibit a strong sense of responsibility in their use of language, despite the ideological influences present in their writing and the ongoing struggles that shape their linguistic and cultural efforts within the framework of the Activist Theory of Language. This study introduces the concept of \"volunteerism\" as a vital component of the Activist Theory of Language, underscoring the dedication and passion that language activists display in their efforts to promote and preserve their indigenous languages. The research is significant as it illuminates the strategies, challenges, and opportunities encountered by Setswana and Punjabi activists, providing valuable insights into the evolving landscape of indigenous language activism on social media. Furthermore, it highlights the critical role of social media in preserving linguistic and cultural heritage and emphasises the need for continuous support and recognition of the tireless work carried out by language activists.