Abstract
The main idea of this paper is to bring out the uniqueness of the speech corpus required for development of command and control applications such as voice-controlled MAV. Since MAV finds application in adverse environments, the effect of noise degrades ASR performance. Since English words uttered are greatly influenced by user’s mother tongue, there is a necessity to create a customized speech corpus. The corpus creation is accomplished by a NALVoiceCorpus tool, which is designed to capture the specific requirements of the corpus. The tool is quite generic in nature and it can find application in development of any ASR system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kang Y, Yuan M (2009) Software design for mini-type ground control station of UAVICEMI’09. In: 9th International conference on electronic measurement and instruments IEEE
Draper M et al (2003) Manual versus speech input for unmanned aerial vehicle control station operations. In: Proceedings of the human factors and ergonomics society annual meeting, vol 47(1). CA: SAGE Publications, Sage CA: Los Angeles
Shrishrimal PP, Deshmukh RR, Waghmare VB (2012) Indian language speech database: a review. Int J Comput Appl 47(5):17–21
Google cloud speech-to-text documentation page. https://cloud.google.com/speech/docs/
Speech recognition in IOS. https://developer.apple.com/documentation/speech
Robust Automatic Transcription of Speech (RATS), for Information Processing Techniques Office (IPTO), Defense Advanced Research Projects Agency (DARPA), DARPA-BAA-10-34, (2012)
Hofbauer K, Petrik S, Hering H (2008) The ATCOSIM corpus of non-prompted clean air traffic control speech. In: LREC
Ayres Tony, Nolan Brian (2006) Voice activated command and control with speech recognition over WiFi. Sci Comput Program 59(1-2):109–126
Li A-J, Yin Z-G (2007) Standardization of speech corpus. Data Sci J 6:806–812
Mission planner overview, ardupilot.org/planner/docs/mission-planner-overview.html
Paliwal KK, Yao K (2010) Robust speech recognition under noisy ambient conditions. In: Human-centric interfaces for ambient intelligence, 135–162
Benzeghiba M et al (2007) Automatic speech recognition and speech variability: a review. Speech Commun 49(10–11):763–786
Flight gear simulator homepage, home.flightgear.org/
Acknowledgements
The authors would like to thank SIGMA panel, AR & DB for funding this activity.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rahul, D.K., Veena, S., Lokesha, H., Lakshmi, P. (2020). Speech Corpus Development for Voice-Controlled MAV. In: Kadambi, G., Kumar, P., Palade, V. (eds) Emerging Trends in Photonics, Signal Processing and Communication Engineering. Lecture Notes in Electrical Engineering, vol 649. Springer, Singapore. https://doi.org/10.1007/978-981-15-3477-5_11
Download citation
DOI: https://doi.org/10.1007/978-981-15-3477-5_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3476-8
Online ISBN: 978-981-15-3477-5
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)