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Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition ...
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Task-Specific Pre-Training and Cross Lingual Transfer for Code-Switched Data ...
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Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching ...
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CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing ...
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Unsupervised Self-Training for Sentiment Analysis of Code-Switched Data ...
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Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems ...
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Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units ...
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Speech technology for unwritten languages
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In: ISSN: 2329-9290 ; EISSN: 2329-9304 ; IEEE/ACM Transactions on Audio, Speech and Language Processing ; https://hal.inria.fr/hal-02480675 ; IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2020, ⟨10.1109/TASLP.2020.2973896⟩ (2020)
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AlloVera: a multilingual allophone database
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In: LREC 2020: 12th Language Resources and Evaluation Conference ; https://halshs.archives-ouvertes.fr/halshs-02527046 ; LREC 2020: 12th Language Resources and Evaluation Conference, European Language Resources Association, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/ (2020)
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Investigating Supplemental Context for Word Sense Disambiguation ...
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Nonlinear ISA with Auxiliary Variables for Learning Speech Representations ...
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Towards Minimal Supervision BERT-based Grammar Error Correction ...
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Towards Zero-shot Learning for Automatic Phonemic Transcription ...
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Automatically Identifying Language Family from Acoustic Examples in Low Resource Scenarios ...
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Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages ...
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Abstract:
With recent advancements in language technologies, humans are now speaking to devices. Increasing the reach of spoken language technologies requires building systems in local languages. A major bottleneck here are the underlying data-intensive parts that make up such systems, including automatic speech recognition (ASR) systems that require large amounts of labelled data. With the aim of aiding development of spoken dialog systems in low resourced languages, we propose a novel acoustics based intent recognition system that uses discovered phonetic units for intent classification. The system is made up of two blocks - the first block is a universal phone recognition system that generates a transcript of discovered phonetic units for the input audio, and the second block performs intent classification from the generated phonetic transcripts. We propose a CNN+LSTM based architecture and present results for two languages families - Indic languages and Romance languages, for two different intent recognition ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2011.03646 https://dx.doi.org/10.48550/arxiv.2011.03646
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