Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recently there has been interest in the approaches for training speech recognition systems for languages with limited resources. Under the IARPA Babel program such resources have been provided for a range of languages to support this research area.  examines a particular form of approach, data augmentation, that can be applied to these situations. Data augmentation schemes aim to increase the quantity of data available to train the system, for example semi-supervised training, multilingual processing, acoustic data perturbation and speech synthesis. To date the majority of work has considered individual data augmentation schemes, with few consistent performance contrasts or examination of whether the schemes are complementary. In this work two data augmentation schemes, semisupervised training and vocal tract length perturbation, are examined and combined on the Babel limited language pack configuration. Here only about 10 hours of transcribed acoustic data are available. Two languages are examined, Assamese and Zulu, which re found to be the most challenging of the Babel languages released for the 2014 Evaluation. For both languages consistent speech recognition performance gains can be obtained using these augmentation schemes. Furthermore the impact of these performance gains on a down-stream keyword spotting task are also described. Index Terms: data augmentation, speech recognition, babel
['Anton Ragni', 'Kate Knill', 'Shakti P. Rath', 'Mark J. F. Gales']
I am writing a research paper on a idea Data augmentation for low resource languages.
[{"from": "human", "value": "I am writing a research paper on a idea Data augmentation for low resource languages."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recently there has been interest in the approaches for training speech recognition systems for languages with limited resources. Under the IARPA Babel program such resources have been provided for a range of languages to support this research area.  examines a particular form of approach, data augmentation, that can be applied to these situations. Data augmentation schemes aim to increase the quantity of data available to train the system, for example semi-supervised training, multilingual processing, acoustic data perturbation and speech synthesis. To date the majority of work has considered individual data augmentation schemes, with few consistent performance contrasts or examination of whether the schemes are complementary. In this work two data augmentation schemes, semisupervised training and vocal tract length perturbation, are examined and combined on the Babel limited language pack configuration. Here only about 10 hours of transcribed acoustic data are available. Two languages are examined, Assamese and Zulu, which re found to be the most challenging of the Babel languages released for the 2014 Evaluation. For both languages consistent speech recognition performance gains can be obtained using these augmentation schemes. Furthermore the impact of these performance gains on a down-stream keyword spotting task are also described. Index Terms: data augmentation, speech recognition, babel"}]