Keyword Recognition Based on MFCC

Sha Yang; Tian Hu; Yun Lu Zhang

doi:10.4028/www.scientific.net/AMR.926-930.1729

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 926-930Keyword Recognition Based on MFCC

Keyword Recognition Based on MFCC

Abstract:

After about 50 years of development, speech recognition technology has been able to achieve large vocabulary, non-specific human continuous speech recognition system. On account of Chinese pronunciation features, we research the small vocabulary, non-specific Chinese speech recognition based on continuous Hidden Markov Model approach. With comparing the datasets of VQ/DTW, VQ/DHMM, CHMM state-1 recognition algorithm and CHMM state-2 recognition algorithm, the results of our experiment show that: (1) CHMM state-2 branch method performs primely in reduction of the recognition time; and (2) the recognition accuracy is improved eventually.

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Periodical:

Advanced Materials Research (Volumes 926-930)

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1729-1732

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https://doi.org/10.4028/www.scientific.net/AMR.926-930.1729

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Online since:

May 2014

Authors:

Sha Yang*, Tian Hu, Yun Lu Zhang

Keywords:

Baum-Walch, Chinese KWR, HMM, Speech Recognition, Viterbi

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References

[1] Myers C S, Rabiner L R, Rosenberg A E. An Investigation of the Use of Dynamic Time Warping for Word Spotting and Connected Word Recognition. ICASSP 80, pp.173-177.

DOI: 10.1109/icassp.1980.1171067

Google Scholar

[2] Higgins Alan L, Wohlord Robert E. Keyword Recognition Using Template Concatenation. ICASSP85, 1985(3) pp.233-1236.

Google Scholar

[3] Wilson J G, Lee C H, Rabiner L R. Application of Hidden Markov Model for Recognation of a Limited Set of Words in Unconstrained Speech. ICASSP85, 1989(3) pp.173-177.

Google Scholar

[4] Wilson Jay G, Rabiner Lawrence R, etal. Automatic Recognation of Key-words in Unconstrained Speech Using Hidden Markov Model. on ASSP, 1990, 38(11) p.1980-(1978).

Google Scholar

[5] A. Martin，GDoddington，T. Kamm，M. ordowski，andM. Przyboeki. The DET Curve in Assessment of Detection Task Performance. In Proc. Eurospeeeh. Rhodes，Greece. 1997, pp.1895-1898.

DOI: 10.21437/eurospeech.1997-504

Google Scholar

[6] Bourlard H，D'hooreB, Boite J-M. Optimizing recognition and rejection Performance in wordspotting systems. IEEE IntemationalConference on Acousties, Speeeh and Signal Proeessing. Adelaide, 1994 pp.373-376.

DOI: 10.1109/icassp.1994.389278

Google Scholar

[7] Jun Guo. A research on speech keyword recognition technology[D]. Beijing: Beijing University of Posts and Telecommunications. (2008).

Google Scholar

[8] Jones GJF，Foote JT, SP arck Jones K, Young SJ(1995) Video Mail Retrieval: the effect of word spotting accuracy on precision. In: Proe. ICASSP95, IEEECSPress, Piscataway, 1 pp.309-12.

DOI: 10.1109/icassp.1995.479535

Google Scholar

[9] Knill K M, Young S J. Speaker dependent keyword spotting for hand-held devices. Technical Report193. Engineering Department，Cambridge University，Cambridge, UK. (1994).

Google Scholar