@ARTICLE{Sakoe1978,
  title = {Dynamic programming algorithm optimization for spoken word recognition},
  author = {Sakoe, H. and Chiba, S.},
  journal = {Acoustics, Speech, and Signal Processing [see also {IEEE} Transactions on Signal Processing], {IEEE} Transactions on},
  year = {1978},
  volume = {26},
  number = {1},
  pages = {43--49},
  month = feb,
  abstract = {This paper reports on an optimum dynamic progxamming (DP) based time-
       normalization algorithm for spoken word recognition. First, a
       general principle of time-normalization is given using time-
       warping function. Then, two time-normalized distance
       definitions, called symmetric and asymmetric forms, are derived
       from the principle. These two forms are compared with each
       other through theoretical discussions and experimental studies.
       The symmetric form algorithm superiority is established. A new
       technique, called slope constraint, is successfully introduced,
       in which the warping function slope is restricted so as to
       improve discrimination between words in different categories.
       The effective slope constraint characteristic is qualitatively
       analyzed, and the optimum slope constraint condition is
       determined through experiments. The optimized algorithm is then
       extensively subjected to experimental comparison with various
       DP-algorithms, previously applied to spoken word recognition by
       different research groups. The experiment shows that the
       present algorithm gives no more than about two-thirds errors,
       even compared to the best conventional algorithm.},
  ISSN = {0096-3518},
}

@ARTICLE{Itakura1975,
  title = {Minimum prediction residual principle applied to speech recognition},
  author = {Itakura, F.},
  journal = {Acoustics, Speech, and Signal Processing [see also {IEEE} Transactions on Signal Processing], {IEEE} Transactions on},
  year = {1975},
  volume = {23},
  number = {1},
  pages = {67--72},
  month = feb,
  abstract = {A computer system is described in which isolated words, spoken by a
       designated talker, are recognized through calculation of a
       minimum prediction residual. A reference pattern for each word
       to be recognized is stored as a time pattern of linear
       prediction coefficients (LPC). The total log prediction
       residual of an input signal is minimized by optimally
       registering the reference LPC onto the input autocorrelation
       coefficients using the dynamic programming algorithm (DP). The
       input signal is recognized as the reference word which produces
       the minimum prediction residual. A sequential decision
       procedure is used to reduce the amount of computation in DP. A
       frequency normalization with respect to the long-time spectral
       distribution is used to reduce effects of variations in the
       frequency response of telephone connections. The system has
       been implemented on a DDP-516 computer for the 200-word
       recognition experiment. The recognition rate for a designated
       male talker is 97.3 percent for telephone input, and the
       recognition time is about 22 times real time.},
  ISSN = {0096-3518},
}

@article{Velichko,
author = {V. M. Velichko and N. G. Zagoruyko},
title = {Automatic Recognition of 200 Words},
journal = {International Journal of Man-Machine Studies},
volume = {2},
issue = {3},
year = {1970},
pages = {223-234},
bibsource = {http://www.interaction-design.org/references/},
}


@ARTICLE{White1976,
  title = {Speech recognition experiments with linear predication, bandpass filtering, and dynamic programming},
  author = {White, G. and Neely, R.},
  journal = {Acoustics, Speech, and Signal Processing [see also {IEEE} Transactions on Signal Processing], {IEEE} Transactions on},
  year = {1976},
  volume = {24},
  number = {2},
  pages = {183--188},
  month = apr,
  abstract = {Automatic speech recognition experiments are described in which
       several popular preprocessing and classification strategies are
       compared. Preprocessing is done either by linear predictive
       analysis or by bandpass filtering. The two approaches are shown
       to produce similar recognition scores. The classifier uses
       either linear time stretching or dynamic programming to achieve
       time alignment. It is shown that dynamic programming is of
       major importance for recognition of polysyllabic words. The
       speech is compressed into a quasi-phoneme character string or
       preserved uncompressed. Best results are obtained with
       uncompressed data, using nonlinear time registration for
       multisyllabic words.},
  ISSN = {0096-3518},
}