@TECHREPORT\{IMM2009-05780,
    author       = "S. Karadogan and J. Larsen and M. S. Pedersen and J. B. Boldt",
    title        = "Robust isolated speech recognition using ideal binary masks",
    year         = "2009",
    month        = "sep",
    keywords     = "isolated sppech recognition, robust, binary mask",
    number       = "",
    series       = "",
    institution  = "Department of Informatics and Mathematical Modelling",
    address      = "Richard Petersens Plads, Building 321, 2800 Kongens Lyngby, Denmark",
    type         = "",
    url          = "http://www2.imm.dtu.dk/pubdb/p.php?5791",
    abstract     = "This is supplementary material for the paper of the same title. 
The paper presents a new approach for robust {ASR} using ideal binary masks as feature vectors. This method is evaluated on a speaker-independent isolated digit database, {TIDIGIT}. Discrete Hidden Markov Model is used for the recognition and the observation vectors are quantized using {K-}means algorithm with Hamming distance. It is found that a recognition rate as high as 92\% for clean speech is achievable using Ideal Binary Masks (IBM). It is also observed that {IBM} feature vectors are robust to different noise
conditions."
}