[1]ZHANG Yi,XIE Yanyi,LUO Yuan,et al.Postprocessing method of MFCC in speech feature extraction[J].CAAI Transactions on Intelligent Systems,2016,11(2):208-215.[doi:10.11992/tis.201511008]
Copy

Postprocessing method of MFCC in speech feature extraction

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 2 Page number: 208-215 Column: 学术论文—机器感知与模式识别 Public date: 2016-04-25
Title:
Postprocessing method of MFCC in speech feature extraction
Author(s):
ZHANG Yi1;  XIE Yanyi2;  LUO Yuan3;  XI Bing3
1. Institute of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
3. College of Opto Electronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Keywords:
postprocessing; phonetic feature; speech recognition; noise; robustness
CLC:
TP391.4
DOI:
10.11992/tis.201511008
Abstract:
To improve the robustness of automatic speech recognition systems, a new speech feature postprocessing method based on the Mel-frequency Cepstral Coefficient (MFCC) is proposed, which is named the MVDA postprocessing method. The postprocessed feature parameters are named MVDAs. This technique combines mean subtraction, variance normalization, time sequence fltering, and autoregressive moving average flters. Experiments were conducted to compare MVDA and MFCC. Changes in the Euclidean distance of the speech with noise and the speech signal were analyzed, proving that every step of MVDA postprocessing could effectively reduce the noise interference. Thus, all MVDAs in different noise environments were superior. This simple feature does not only achieve the effect of many complex speech feature processing methods but also effectively reduces the computational complexity of automatic speech recognition systems.
References:
[1] PALIWAL K K, BASU A. A speech enhancement method based on Kalman fltering[C]//Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. Dallas, USA, 1997: 177-180.
[2] GIBSON J D, KOO B, GRAY S D. Filtering of Colored Noise for Speech Enhancement and Coding[J]. IEEE Transactions on Signal Processing, 1991, 39(8): 1732-1742.
[3] ZELINSKI R. A microphone array with adaptive post-filtering for noise reduction in reverberant rooms[C]//Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. New York, USA, 1998: 2578-2581.
[4] MYLLYMAKI M, VIRTANEN T. Non-stationary noise model compensation in voice activity detection[C]//Proceedings of IEEE International Conference on Signal Processing Conference. Glasgow, Scotland, 2009: 2186-2190.
[5] RAMFREZ J, SEGURA J C, BENFTEZ C, et al. Efficient voice activity detection algorithms using long-term speech information[J]. Speech communication, 2004, 42(3/4): 271-287.
[6] CHOWDHURY M, SELOUANI S A, O’SHAUGHNESSY D. A soft computing approach to improve the robustness of on-line ASR in previously unseen highly non-stationary acoustic environments[C]//Proceedings of the 11th IEEE International Conference on Information Science, Signal Processing and their Applications. Montreal, Canada, 2012: 522-527.
[7] GUPTA H A, RAJU A, ALWAN A. Non-linear dimension reduction of Gabor features for noise-robust ASR[C]//Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. Florence, Italy, 2014: 1715-1719.
[8] HANSEN J H L, VARADARAJAN V. Analysis and compensation of lombard speech across noise type and levels with application to in-set/out-of-set speaker recognition[J]. IEEE transactions on audio, speech, and language processing, 2009, 17(2): 366-378.
[9] COOK G, ROBINSON T. Transcribing broadcast news with the 1997 abbot system[C]//Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. Seattle, USA, 1998: 917-920.
[10] KIM D S, LEE S Y, KIL R M. Auditory processing of speech signals for robust speech recognition in real-world noisy environments[J]. IEEE transactions on speech and audio processing, 1999, 7(1): 55-69.
[11] HAIN T, WOODLAND P C, EVERMANN G, et al. New features in the CU-HTK system for transcription of conversational telephone speech[C]//Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. Salt Lake City, UT, 2001(1): 57-60.
[12] LIN S H, CHEN B, YEH Y M. Exploring the use of speech features and their corresponding distribution characteristics for robust speech recognition[J]. IEEE transactions on audio, speech, and language processing, 2009, 17(1): 84-94.
[13] MORTIA S, UNOKI M, LU Xugang, et al. Robust voice activity detection based on concept of modulation transfer function in noisy reverberant environments[C]//Proceedings of International Symposium on Chinese Spoken Language Processing (ISCSLP). Singapore, 2014: 108-112.
[14] CHANG J E, BAI J Y, ZENG Fangang. Unintelligible low frequency sound enhances simulated cochlear implant speech recognition in noise[J]. IEEe transactions on biomedical engineering, 2006, 53(12): 2598-2601.
[15] BOLL S F. Suppression of acoustic noise in speech using spectral subtraction[J]. IEEE transactions on acoustics, speech, and signal processing, 1999, 27(2): 113-120.
[16] MAMMONE R J, ZHANG Xiaoyu, RAMACHANDRAN R P. Robust speaker recognition: a feature-based approach[J]. IEEE signal processing magazine, 1996, 13(5): 58-71.
[17] BOLL S F. Suppression of acoustic noise in speech using spectral subtraction[J]. IEEE transactions on acoustics, speech, and signal processing, 1999, 27(2): 113-120.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems