Data augmentation for low resource languages

By Anton Ragni, K. Knill, Shakti Prasad Rath, Mark John Francis Gales. 2014

158 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Signal Processing

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Citations in the corpus

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition (2019, background): “For example, in [9, 10], artificial data was augmented for low resource speech recognition tasks.”
Specaugment on Large Scale Datasets (2019, background): “Artiﬁcial data augmentation for low resource speech recognition tasks has been studied in [6, 7].”
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain (2020, methodology): “Data augmentation is a common method to improve supervised training in speech applications, especially in low resource settings [25, 26].”
Multi-Modal Data Augmentation for End-to-end ASR (2018, background): “Such perturbations include Vocal Tract Length Perturbations (VTLN) as in [14] to expose the ASR to a variety of synthetic speaker variations, as well as speed, tempo and volume perturbations [15].”
Audio augmentation for speech recognition (2015, methodology): “In [5, 6] the use of data augmentation on low resource languages, where the amount of training data is comparatively small (∼ 10 hrs), was investigated.”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, unknown): “[324].”
Data Augmentation for Deep Neural Network Acoustic Modeling (2015, background): “DATA AUGMENTATION APPROACHES A. Vocal Tract Length Perturbation (VTLP) VTLP, which was .rst proposed in [18], is representative of a group of data augmentation schemes that generate new samples through perturbing or distorting the speech spectra of the existing training samples.”
Ensemble Self-Training for Low-Resource Languages: Grapheme-to-Phoneme Conversion and Morphological Inflection (2020, methodology): “When annotation is difﬁcult to obtain, data augmentation is a common practice to increase training data size with reasonable quality to feed to powerful models (Ragni et al., 2014; Bergmanis et al., 2017; Silfverberg et al., 2017).”
Pretraining by Backtranslation for End-to-End ASR in Low-Resource Settings (2018, methodology): “Bolstering the decoder with a language model (LM) trained on supplemental text data is one such method that improves end-to-end ASR performance [4, 5]; however, more significant gains can be obtained by training on additional synthetically perturbed speech [6, 7, 8], or by multilingual training, whi”
Low-Resource Speech Recognition and Keyword-Spotting (2017, background): “These approaches include data augmentation [23,5,14]; the use of web data [20]; extensive system combination [31]; and the use of multiple languages [4,24].”
Speech recognition and keyword spotting for low-resource languages: Babel project research at CUED (2014, background): “For further information about the experimental configuration, and additional results, see [38].”
Data Augmentation via Dependency Tree Morphing for Low-Resource Languages (2018, methodology): “Similarly, speech recognition systems made use of augmentation techniques like changing the tone and speed of the audio (Ko et al., 2015; Ragni et al., 2014), noise addition (Hartmann et al., 2016) and synthetic audio generation (Takahashi et al., 2016).”
Low Resource Multi-modal Data Augmentation for End-to-end ASR (2018, methodology): “Bolstering the decoder with a language model (LM) trained on supplemental text data is one such method that improves end-to-end ASR performance [4]; however, more significant gains can be obtained by training on additional synthetically perturbed speech [5, 6, 7], or by multilingual training, which ”
Realistic Multi-Microphone Data Simulation for Distant Speech Recognition (2016, background): “Approaches as contaminated speech training [14, 15, 16], multi-style training [17, 18, 19] and data augmentation [20, 21, 22, 23] have, in fact, been shown very effective in improving the DSR system performance.”