Automating primary stress identification has been an active research field due to the role of stress in encoding meaning and aiding speech comprehension. Previous studies relied mainly on traditional acoustic features and English datasets. In this paper, we investigate the approach of fine-tuning a pre-trained transformer model with an audio frame classification head. Our experiments use a new Croatian training dataset, with test sets in Croatian, Serbian, the Chakavian dialect, and Slovenian. By comparing an SVM classifier using traditional acoustic features with the fine-tuned speech transformer, we demonstrate the transformer's superiority across the board, achieving near-perfect results for Croatian and Serbian, with a 10-point performance drop for the more distant Chakavian and Slovenian. Finally, we show that only a few hundred multi-syllabic training words suffice for strong performance. We release our datasets and model under permissive licenses.

Resources in high-resource languages have not been efficiently exploited in low-resource languages to solve language-dependent research problems. Spanish and French are considered high resource languages in which an adequate level of data resources for informal online social behavior modeling, is observed. However, a machine translation system to access those data resources and transfer their context and tone to a low-resource language like dialectal Arabic, does not exist. In response, we propose a framework that localizes contents of high-resource languages to a low-resource language/dialects by utilizing AI power. To the best of our knowledge, we are the first work to provide a parallel translation dataset from/to informal Spanish and French to/from informal Arabic dialects. Using this, we aim to enrich the under-resource-status dialectal Arabic and fast-track the research of diverse online social behaviors within and across smart cities in different geo-regions. The experimental results have illustrated the capability of our proposed solution in exploiting the resources between high and low resource languages and dialects. Not only this, but it has also been proven that ignoring dialects within the same language could lead to misleading analysis of online social behavior.

Italy is characterized by a one-of-a-kind linguistic diversity landscape in Europe, which implicitly encodes local knowledge, cultural traditions, artistic expressions and history of its speakers. However, most local languages and dialects in Italy are at risk of disappearing within few generations. The NLP community has recently begun to engage with endangered languages, including those of Italy. Yet, most efforts assume that these varieties are under-resourced language monoliths with an established written form and homogeneous functions and needs, and thus highly interchangeable with each other and with high-resource, standardized languages. In this paper, we introduce the linguistic context of Italy and challenge the default machine-centric assumptions of NLP for Italy's language varieties. We advocate for a shift in the paradigm from machine-centric to speaker-centric NLP, and provide recommendations and opportunities for work that prioritizes languages and their speakers over technological advances. To facilitate the process, we finally propose building a local community towards responsible, participatory efforts aimed at supporting vitality of languages and dialects of Italy.

With a major focus on its history, difficulties, and promise, this research paper provides a thorough analysis of the chatbot technology environment as it exists today. It provides a very flexible chatbot system that makes use of reinforcement learning strategies to improve user interactions and conversational experiences. Additionally, this system makes use of sentiment analysis and natural language processing to determine user moods. The chatbot is a valuable tool across many fields thanks to its amazing characteristics, which include voice-to-voice conversation, multilingual support [12], advising skills, offline functioning, and quick help features. The complexity of chatbot technology development is also explored in this study, along with the causes that have propelled these developments and their far-reaching effects on a range of sectors. According to the study, three crucial elements are crucial: 1) Even without explicit profile information, the chatbot system is built to adeptly understand unique consumer preferences and fluctuating satisfaction levels. With the use of this capacity, user interactions are made to meet their wants and preferences. 2) Using a complex method that interlaces Multiview voice chat information, the chatbot may precisely simulate users' actual experiences. This aids in developing more genuine and interesting discussions. 3) The study presents an original method for improving the black-box deep learning models' capacity for prediction. This improvement is made possible by introducing dynamic satisfaction measurements that are theory-driven, which leads to more precise forecasts of consumer reaction.

Sourcing Specialist at Appen ACTIVELY HIRING Let's connect! Apply and join our team today! We are currently looking for Candidates in different Languages and Dialects in #Greece. Languages and Dialects: #Turkish (Turkey) #Greek (Greece) #German (Germany) #English (United States) Are you ready to help drive advances in computer vision applications for a major technology company? If you have a smartphone and enjoy taking photos then this project is perfect for you.

Sourcing Specialist at Appen ACTIVELY HIRING Let's connect! We are currently looking for Candidates in different Languages and Dialects in #Greece Languages and Dialects: #German (Germany) #Turkish (Turkey) #Greek (Greece) Are you ready to help drive advances in computer vision applications for a major technology company? If you have a smartphone and enjoy videos then this project is perfect for you. We are looking for several video submissions of different rooms in your home environment with the main lighting switched ON and NOT from battery-powered or natural light. These will be taken on your smartphone devices and uploaded through our platform.

Microsoft announced today that 12 new languages and dialects have been added to Translator. These additions mean that the service can now translate between more than 100 languages and dialects, making information in text and documents accessible to 5.66 billion people worldwide. "One hundred languages is a good milestone for us to achieve our ambition for everyone to be able to communicate regardless of the language they speak," said Xuedong Huang, Microsoft technical fellow and Azure AI chief technology officer. Translator today covers the world's most spoken languages including English, Chinese, Hindi, Arabic and Spanish. In recent years, advances in AI technology have allowed the company to grow its language library with low-resource and endangered languages, such as Inuktitut, a dialect of Inuktut that is spoken by about 40,000 Inuit in Canada.

Speech-to-text applications have never been so plentiful, popular or powerful, with researchers' pursuit of ever-better automatic speech recognition (ASR) system performance bearing fruit thanks to huge advances in machine learning technologies and the increasing availability of large speech datasets. Current speech recognition systems require thousands of hours of transcribed speech to reach acceptable performance. However, a lack of transcribed audio data for the less widely spoken of the world's 7,000 languages and dialects makes it difficult to train robust speech recognition systems in this area. To help ASR development for such low-resource languages and dialects, Facebook AI researchers have open-sourced the new wav2vec 2.0 algorithm for self-supervised language learning. The paper Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations claims to "show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler." A Facebook AI tweet says the new algorithm can enable automatic speech recognition models with just 10 minutes of transcribed speech data.

Natural language processing (NLP) research predominantly focuses on developing methods that work well for English despite the many positive benefits of working on other languages. These benefits range from an outsized societal impact to modelling a wealth of linguistic features to avoiding overfitting as well as interesting challenges for machine learning (ML). There are around 7,000 languages spoken around the world. The map above (see the interactive version at Langscape) gives an overview of languages spoken around the world, with each green circle representing a native language. Most of the world's languages are spoken in Asia, Africa, the Pacific region and the Americas.

Homegrown internet startup Hike describes that social products should be joyful. They should be built around people and not the other way around. They should be fun and should celebrate the depth of relationships. They should allow people to be their true selves and go beyond the limits that hold them back in the real world. And following the same, their messenger app is built around the same values and policies.