Application of local binary patterns for SVM based stop consonant detection

Kaizhi Qian, Yang Zhang, Mark Hasegawa-Johnson

Research output: Contribution to journalConference articlepeer-review


Detection of acoustic phonetic landmarks is useful for a variety of speech processing applications such as automatic speech recognition.The majority of existing methods use Melfrequency Cepstral Coefficients (MFCCs) describing the short time power spectral envelope of the speech signal. This paper hypothesizes that a different feature extraction method can be used to complement MFCCs by capturing more complex transient acoustic cues. The proposed feature extraction method quantizes spectrogram textures using local binary patterns (LBP). This paper particularly exploits landmark based stop consonant detection. Both methods outperform the previous work on stop consonant detection and the latter is particularly appealing for real time detection in which computation efficiency matters.

Original languageEnglish (US)
Pages (from-to)1114-1118
Number of pages5
JournalProceedings of the International Conference on Speech Prosody
StatePublished - 2016
Event8th Speech Prosody 2016 - Boston, United States
Duration: May 31 2016Jun 3 2016


  • Acoustic phonetic landmark detection
  • Local binary pattern
  • Stop consonants
  • Time-frequency features

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language


Dive into the research topics of 'Application of local binary patterns for SVM based stop consonant detection'. Together they form a unique fingerprint.

Cite this