Prodorshok I: A Bengali Isolated Speech Dataset for Voice-Based Assistive Technologies - A comparative analysis of the effects of data augmentation on HMM-GMM and DNN classifiers
This work addresses the need for speaker-independent ASR in Bengali to enhance human-computer interaction, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of creating a Bengali isolated speech dataset for voice-based assistive technologies and found that simple data augmentation with pitch shift significantly improves accuracy in speech recognition systems.
Prodorshok I is a Bengali isolated word dataset tailored to help create speaker-independent, voice-command driven automated speech recognition (ASR) based assistive technologies to help improve human-computer interaction (HCI). This paper presents the results of an objective analysis that was undertaken using a subset of words from Prodorshok I to assess its reliability in ASR systems that utilize Hidden Markov Models (HMM) with Gaussian emissions and Deep Neural Networks (DNN). The results show that simple data augmentation involving a small pitch shift can make surprisingly tangible improvements to accuracy levels in speech recognition.