rastamat

PLP and RASTA (and MFCC, and inversion) in Matlab using melfcc.m and invmelfcc.m. .. Another popular speech feature representation is known as RASTA-PLP, an acronym for Relative Spectral Transform - Perceptual Linear Prediction. PLP was originally proposed by Hynek Hermansky as a way of warping spectra to minimize the differences between speakers while preserving the important speech information [Herm90]. RASTA is a separate technique that applies a band-pass filter to the energy in each frequency subband in order to smooth over short-term noise variations and to remove any constant offset resulting from static spectral coloration in the speech channel e.g. from a telephone line [HermM94]. ..


References in zbMATH (referenced in 28 articles )

Showing results 1 to 20 of 28.
Sorted by year (citations)

1 2 next

  1. Lin, Chen-Yun; Su, Li; Wu, Hau-Tieng: Wave-shape function analysis. When cepstrum meets time-frequency analysis (2018)
  2. Atkins, Jamin; Sharma, Davinder Pal: Visualization of babble-speech interactions using Andrews curves (2016) ioport
  3. Karpukhin, I. A.: Contribution from the accuracy of phoneme recognition to the quality of automatic recognition of Russian speech (2016)
  4. Gonzalez-Dominguez, Javier; Lopez-Moreno, Ignacio; Moreno, Pedro J.; Gonzalez-Rodriguez, Joaquin: Frame-by-frame language identification in short utterances using deep neural networks (2015) ioport
  5. Pitsikalis, Vassilis; Katsamanis, Athanasios; Theodorakis, Stavros; Maragos, Petros: Multimodal gesture recognition via multiple hypotheses rescoring (2015)
  6. Krey, Sebastian; Ligges, Uwe; Leisch, Friedrich: Music and timbre segmentation by recursive constrained (K)-means clustering (2014)
  7. Lee, Jong-Seok: Visual-speech-pass filtering for robust automatic lip-Reading (2014) ioport
  8. Schafer, Phillip B.; Jin, Dezhe Z.: Noise-robust speech recognition through auditory feature detection and spike sequence decoding (2014)
  9. Choi, Yong-Sun; Lee, Soo-Young: Nonlinear spectro-temporal features based on a cochlear model for automatic speech recognition in a noisy situation (2013) ioport
  10. Kurian, Cini; Balakrishnan, Kannan: Connected digit speech recognition system for Malayalam language (2013) ioport
  11. Shen, Haifeng; Liu, Gang; Guo, Jun: Two-stage model-based feature compensation for robust speech recognition (2012)
  12. Ligges, Uwe; Krey, Sebastian: Feature clustering for instrument classification (2011)
  13. Vorwerk, Alexander; Zeiler, Steffen; Kolossa, Dorothea; Fernandez Astudillo, Ramón; Lerch, Dennis: Use of missing and unreliable data for audiovisual speech recognition (2011)
  14. Joshi, Neil; Guan, Ling: Feature fusion applied to missing data ASR with the combination of recognizers (2010) ioport
  15. Lü, Yong; Wu, Haiyang; Zhou, Lin; Wu, Zhenyang: Multi-environment model adaptation based on vector Taylor series for robust speech recognition (2010)
  16. Mahdi, Abdulhussain E.; Picovici, Dorel: New single-ended objective measure for non-intrusive speech quality evaluation (2010) ioport
  17. Minematsu, Nobuaki; Asakawa, Satoshi; Suzuki, Masayuki; Qiao, Yu: Speech structure and its application to robust speech processing (2010)
  18. Ren, Yao; Johnson, Michael T.; Clemins, Patrick J.; Darre, Michael; Glaeser, Sharon Stuart; Osiejuk, Tomasz S.; Out-Nyarko, Ebenezer: A framework for bioacoustic vocalization analysis using hidden Markov models (2009)
  19. Hyassat, Hussein; Abu Zitar, Raed: Arabic speech recognition using SPHINX engine (2008) ioport
  20. O’Shaughnessy, Douglas: Invited paper: Automatic speech recognition: History, methods and challenges (2008)

1 2 next