Abstract
Human voice is an important concern of efficient and modern communication in the era of Alexa, Siri, or Google Assistance. Working with voice or speech is going to be easy by preprocessing the unwanted entities when real speech data contains a lot of noise or continuous delivery of a speech. Working with Bangla language is also a concern of enriching the scope of efficient communication over Bangla language. This paper presented a method to reduce noise from speech data collected from a random noisy place, and segmentation of word from continuous Bangla voice. By filtering the threshold of noise with fast Fourier transform (FFT) of audio frequency signal for reduction of noise and compared each chunk of audio signal with minimum dBFS value to separate silent period and non-silent period and on each silent period, segment the signal for word segmentation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Soonil, K., Kim, S.-J., Choeh, J.Y.: Preprocessing for elderly speech recognition of smart devices. Comput. Speech Lang. (2015). 36.10.1016/j.csl.2015.09.002
Banglapedia, Bangla Language.: (2016 August 30). Available http://en.banglapedia.org/index.php?title=Bangla_Language
Igarashi, T., Hughes, J.: Voice as sound: using non-verbal voice input for interactive control. (2002). https://doi.org/10.1145/502348.502372
Hirsch, H.-G., Ehrlicher, C.: Noise estimation techniques for robust speech recognition. In: ICASSP IEEE International Conference on Acoustics, Speech and Signal Processing—Proceedings. vol. 1, pp. 153–156. https://doi.org/10.1109/icassp.1995.479387
Umapathy, K., Krishnan, S., Parsa, V., Jamieson, D.: Discrimination of pathological voices using a time-frequency approach. IEEE Trans. Bio-Med. Eng. 52, 421–430 (2005). https://doi.org/10.1109/TBME.2004.842962
Maeran, O., Piuri, V., Gajani, Giancarlo.: Speech recognition through phoneme segmentation and neural classification. 2, 1215–1220 (1997). https://doi.org/10.1109/imtc.1997.612392
Prodeus, A.M.: Performance measures of noise reduction algorithms in voice control channels of UAVs. In: 2015 IEEE International Conference Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD), Kiev, pp. 189–192 (2015)
Le Bouquin Jeannès, R., Faucon, G.: Study of a voice activity detector and its influence on a noise reduction system. Speech Commun. 16, 245–254 (1995). https://doi.org/10.1016/0167-6393(94)00056-G
Brueckmann, R., Scheidig, A., Gross, H.-M.: Adaptive noise reduction and voice activity detection for improved verbal human-robot interaction using binaural data. pp. 1782–1787 (2007). https://doi.org/10.1109/robot.2007.363580
Linhard, K.: Noise-reduction method for noise-affected voice channels. J. Acoust. Soc. Am. 98 (1995). https://doi.org/10.1121/1.413359
Wang, A., Smith, J.: Systems and methods for recognizing sound and music signals in high noise and distortion (2014)
Nierhaus, F.P., Vandermersch, P.: Method for background noise reduction and performance improvement in voice conferencing over packetized networks. US 2003/0063572A1, Apr 3, 2003 (2008)
Kang, G.S., Heide, D.A.: Acoustic noise reduction for speech communication: (second-order gradient microphone). In: 1999 IEEE International Symposium on Circuits and Systems (ISCAS), Orlando, FL, vol. 4, pp. 556–559 (1999)
Plapous, C., Marro, C., Mauuary, L., Scalart, P.: A two-step noise reduction technique. In: 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, Que, pp. I-289 (2004)
Dyba, R.A., Su, W.W., Deng, H.: Fixed-point implementation of noise reduction using starcore—SC3400. In: 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, Marco Island, FL, pp. 194–199 (2009)
Kim, J., Han, S., Lee, D., Hahn, M.: Adaptive noise reduction algorithm on smart devices in pervasive home environment for voice communication service. In: 2008 5th IEEE Consumer Communications and Networking Conference, Las Vegas, NV, pp. 1210–1211 (2008)
Pramanik, A., Raha, R.: De-noising/noise cancellation mechanism for sampled speech/voice signal. In: 2012 Ninth International Conference on Wireless and Optical Communications Networks (WOCN), Indore, pp. 1–4 (2012)
Cheikh-Rouhou, F., Tilp, J.: Two-channel noise reduction with pitch-adaptive post-processing. 2 (2000). https://doi.org/10.1109/acssc.2000.911286
Agarwal, T., Kabal, P.: Preprocessing of noisy speech for voice coders. pp. 169–171 (2002). https://doi.org/10.1109/scw.2002.1215761
Xu, Z., Bai, F., Bang, G.: Improved variable ZA-LMS algorithm based on discrete cosine transform for high noise reduction. pp. 5116–5121 (2016). https://doi.org/10.1109/chicc.2016.7554149
André-Obrecht, Régine: A new statistical approach for the automatic segmentation of continous speech signals. Acou. Speech Signal Proc. IEEE Trans. 36, 29–40 (1988). https://doi.org/10.1109/29.1486
Mermelstein, Paul: Automatic segmentation of speech into syllabic units. J. Acou. Soc. Am. 58, 880–883 (1975). https://doi.org/10.1121/1.380738
Godard, P., Zanon Boito, M., Ondel, L., Berard, A., Yvon, F., Villavicencio, A., Besacier, L.: Unsupervised word segmentation from speech with attention. 2678–2682 (2018). https://doi.org/10.21437/interspeech.2018-1308
Gold, K., Scassellati, B.: Audio speech segmentation without language-specific knowledge (2006)
Yurovsky, Daniel, Yu, Chen, Smith, Linda: Statistical speech segmentation and word learning in parallel: scaffolding from child-directed speech. Front. Psychol. 3, 374 (2012). https://doi.org/10.3389/fpsyg.2012.0037
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Fahad hossain, M., Mehedi Hasan, M., Ali, H., Abujar, S. (2021). A Continuous Word Segmentation of Bengali Noisy Speech. In: Borah, S., Pradhan, R., Dey, N., Gupta, P. (eds) Soft Computing Techniques and Applications. Advances in Intelligent Systems and Computing, vol 1248. Springer, Singapore. https://doi.org/10.1007/978-981-15-7394-1_48
Download citation
DOI: https://doi.org/10.1007/978-981-15-7394-1_48
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7393-4
Online ISBN: 978-981-15-7394-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)