arXiv Research
Abstract:…have huge amount of audio data which can be used for achieving valuable business insights and transcription of phone calls is manually tedious task. An effective AutomatedSpeech…▽ MoreCall Centers have huge amount of audio data which can be used for achieving valuable business insights and transcription of phone calls is manually tedious task. An effective AutomatedSpeechRecognitionsyste
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arXiv Research
Abstract:…Llama 2 7B-parameter model, recently released by Meta GenAI. We fine-tune the LLM using datasets with different sizes, imbalance degrees, and languages (i.e., English, RomanUrduand…▽ MoreDetecting online sexual predatory behaviours and abusive language on social media platforms has become a critical area of research due to the growing concerns about online safety, especially for vulnerab
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arXiv Research
Abstract:This research investigates the transferability of AutomaticSpeechRecognition(ASR)-robust Natural Language Understanding (NLU) models from controlled experimental conditions to practical, real-world applications. Focused on smart home automation commands inUrdu, the study asse…▽ MoreThis research investigates the transferability of AutomaticSpeechRecognition(ASR)-robust Natural Language Un
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arXiv Research
Abstract:This paper presents a comprehensive evaluation ofUrduAutomatic…▽ MoreThis paper presents a comprehensive evaluation ofUrduAutomaticSpeechRecognition(ASR) models. We analyze the performance of three ASR model families: Whisper, MMS, and Seamless-M4T using Word Error Rate (WER), along with a detailed examination of the most frequent wrong words and error types including insertions, deletion
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arXiv Research
Abstract:AutomaticSpeech…▽ MoreAutomaticSpeechRecognition(ASR) technology has witnessed significant advancements in recent years, revolutionizing human-computer interactions. While major languages have benefited from these developments, lesser-resourced languages likeUrduface unique challenges. This paper provides an extensive exploration of the dynamic landscape of ASR research, focusing particul
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arXiv Research
Abstract:This study evaluates LargeLanguage…▽ MoreThis study evaluates LargeLanguageModels' (LLMs) ability to simulate non-native-like English use observed in human secondlanguage(L2) learners interfered with by their native firstlanguage(L1). In dialogue-based interviews, we prompt LLMs to mimic L2 English learners with specific L1s (e.g., Japanese, Thai,Urdu) across sevenlanguages, comparing the
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arXiv Research
Abstract:Multilingual LargeLanguage…▽ MoreMultilingual LargeLanguageModels(LLMs) often provide suboptimal performance on low-resourcelanguageslikeUrdu. This paper introduces UrduLLaMA 1.0, amodelderived from the open-source Llama-3.1-8B-Instruct architecture and continually pre-trained on 128 millionUrdutokens, capturing the rich diversity of thelanguage. To enhance instruction-following and trans
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arXiv Research
Abstract:As the Information Retrieval (IR) field increasingly recognizes the importance of inclusivity, addressing the needs of low-resourcelanguagesremains a significant challenge. Transliteration between…▽ MoreAs the Information Retrieval (IR) field increasingly recognizes the importance of inclusivity, addressing the needs of low-resourcelanguagesremains a significant challenge. Transliteration
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arXiv Research
Abstract:Question Answering (QA) tasks, which involve extracting answers from a given context, are relatively straightforward for modern LargeLanguage…▽ MoreQuestion Answering (QA) tasks, which involve extracting answers from a given context, are relatively straightforward for modern LargeLanguageModels(LLMs) when the context is short. However, long contexts pose challenges due to the quadratic co
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arXiv Research
Abstract:The Latin script is often used to informally writelanguageswith non-Latin native scripts. In many cases (e.g., most…▽ MoreThe Latin script is often used to informally writelanguageswith non-Latin native scripts. In many cases (e.g., mostlanguagesin India), the lack of conventional spelling in the Latin script results in high spelling variability. Such romanization renderslanguagesthat are
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arXiv Research
Abstract:…content, particularly text and images on social media, has positioned Multimodal Named Entity Recognition (MNER) as an increasingly important area of research within NaturalLanguageProcessing. Despite progress in high-resource…▽ MoreThe emergence of multimodal content, particularly text and images on social media, has positioned Multimodal Named Entity Recognition (MNER) as an increasing
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arXiv Research
Abstract:Multifarious intent detection predictors are developed for differentlanguages, including English, Chinese and French, however, the field remains underdeveloped for…▽ MoreMultifarious intent detection predictors are developed for differentlanguages, including English, Chinese and French, however, the field remains underdeveloped forUrdu, the 10th most spokenlanguage. In the realm of well-k
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