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

• Published in: 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) pp. 396 - 399
• Main Authors: Reza, Mohi, Rashid, Warida, Mostakim, Moin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2017
• Subjects: Assistive Technology; Automatic Speech Recognition; Bengali; Deep Neural Network; Discrete Fourier transforms; Feature extraction; Filter banks; Gaussian Mixture Model; Hidden Markov Model; Hidden Markov models; Human Computer Interaction; Mel frequency cepstral coefficient; Speech; Speech recognition
• ISSN: 2572-7621
• DOI: 10.1109/R10-HTC.2017.8288983

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.