Speech recognition of south China languages based on federated learning and mathematical construction

As speech recognition technology continues to advance in sophistication and computer processing power, more and more recognition technologies are being integrated into a variety of software platforms, enabling intelligent speech processing. We create a comprehensive processing platform for multilingual resources used in business and security fields based on speech recognition and distributed processing technology. Based on the federated learning model, this study develops speech recognition and its mathematical model for languages in South China. It also creates a speech dataset for dialects in South China, which at present includes three dialects of Mandarin and Cantonese, Chaoshan and Hakka that are widely spoken in the Guangdong region. Additionally, it uses two data enhancement techniques—audio enhancement and spectrogram enhancement—for speech signal characteristics in order to address the issue of unequal label distribution in the dataset. With a macro-average F-value of 91.54% and when compared to earlier work in the field, experimental results show that this structure is combined with hyperbolic tangent activation function and spatial domain attention to propose a dialect classification model based on hybrid domain attention

NusaCrowd: Open Source Initiative for Indonesian NLP Resources

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken

Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization

Automatic speech recognition (ASR) has recently become an important challenge when using deep learning (DL). It requires large-scale training datasets and high computational and storage resources. Moreover, DL techniques and machine learning (ML) approaches in general, hypothesize that training and testing data come from the same domain, with the same input feature space and data distribution characteristics. This assumption, however, is not applicable in some real-world artificial intelligence (AI) applications. Moreover, there are situations where gathering real data is challenging, expensive, or rarely occurring, which can not meet the data requirements of DL models. deep transfer learning (DTL) has been introduced to overcome these issues, which helps develop high-performing models using real datasets that are small or slightly different but related to the training data. This paper presents a comprehensive survey of DTL-based ASR frameworks to shed light on the latest developments and helps academics and professionals understand current challenges. Specifically, after presenting the DTL background, a well-designed taxonomy is adopted to inform the state-of-the-art. A critical analysis is then conducted to identify the limitations and advantages of each framework. Moving on, a comparative study is introduced to highlight the current challenges before deriving opportunities for future research

The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages

Instruction tuned large language models (LLMs), such as ChatGPT, demonstrate remarkable performance in a wide range of tasks. Despite numerous recent studies that examine the performance of instruction-tuned LLMs on various NLP benchmarks, there remains a lack of comprehensive investigation into their ability to understand cross-lingual sociopragmatic meaning (SM), i.e., meaning embedded within social and interactive contexts. This deficiency arises partly from SM not being adequately represented in any of the existing benchmarks. To address this gap, we present SPARROW, an extensive multilingual benchmark specifically designed for SM understanding. SPARROW comprises 169 datasets covering 13 task types across six primary categories (e.g., anti-social language detection, emotion recognition). SPARROW datasets encompass 64 different languages originating from 12 language families representing 16 writing scripts. We evaluate the performance of various multilingual pretrained language models (e.g., mT5) and instruction-tuned LLMs (e.g., BLOOMZ, ChatGPT) on SPARROW through fine-tuning, zero-shot, and/or few-shot learning. Our comprehensive analysis reveals that existing open-source instruction tuned LLMs still struggle to understand SM across various languages, performing close to a random baseline in some cases. We also find that although ChatGPT outperforms many LLMs, it still falls behind task-specific finetuned models with a gap of 12.19 SPARROW score. Our benchmark is available at: https://github.com/UBC-NLP/SPARROWComment: Accepted by EMNLP 2023 Main conferenc

The automatic processing of multiword expressions in Irish

It is well-documented that Multiword Expressions (MWEs) pose a unique challenge to a variety of NLP tasks such as machine translation, parsing, information retrieval, and more. For low-resource languages such as Irish, these challenges can be exacerbated by the scarcity of data, and a lack of research in this topic. In order to improve handling of MWEs in various NLP tasks for Irish, this thesis will address both the lack of resources specifically targeting MWEs in Irish, and examine how these resources can be applied to said NLP tasks. We report on the creation and analysis of a number of lexical resources as part of this PhD research. Ilfhocail, a lexicon of Irish MWEs, is created through extract- ing MWEs from other lexical resources such as dictionaries. A corpus annotated with verbal MWEs in Irish is created for the inclusion of Irish in the PARSEME Shared Task 1.2. Additionally, MWEs were tagged in a bilingual EN-GA corpus for inclusion in experiments in machine translation. For the purposes of annotation, a categorisation scheme for nine categories of MWEs in Irish is created, based on combining linguistic analysis on these types of constructions and cross-lingual frameworks for defining MWEs. A case study in applying MWEs to NLP tasks is undertaken, with the exploration of incorporating MWE information while training Neural Machine Translation systems. Finally, the topic of automatic identification of Irish MWEs is explored, documenting the training of a system capable of automatically identifying Irish MWEs from a variety of categories, and the challenges associated with developing such a system. This research contributes towards a greater understanding of Irish MWEs and their applications in NLP, and provides a foundation for future work in exploring other methods for the automatic discovery and identification of Irish MWEs, and further developing the MWE resources described above

2010-2011, University of Memphis bulletin

University of Memphis bulletin containing the graduate catalog for 2010-2011.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1430/thumbnail.jp

Employees on social media: A multi-spokespeople model of CSR communication

Increasing societal and stakeholder expectations, along with easy access to information through social media, means corporations are asked for more information. The traditional approach to CSR communication, with corporations controlling what and how much to share with stakeholders has been restructured by social media, with stakeholders taking control. As legitimacy on social media is created through the positive and negative judgements of stakeholders, corporations must plan how to meet stakeholder demands for information effectively and legitimately, and this includes choosing appropriate spokespeople. Corporations in India have now turned towards their employees as CSR spokespeople. By encouraging employee activity on social media, these corporations are attempting to meet stakeholder demands and generate legitimacy through spokespeople whom stakeholders perceive as equals. This article examines that strategy and discusses its viability of using employees as spokespeople for CSR communication and engagement with stakeholder

Bowdoin College Catalogue and Academic Handbook (2023-2024)

https://digitalcommons.bowdoin.edu/course-catalogues/1321/thumbnail.jp