Abstract-Traditionally, static mel-frequency cepstral coefficients (MFCCs) are derived by discrete cosine transformation (DCT), and dynamic MFCCs are derived by linear regression. Their derivation may be generalized as a frequency-domain transformation of the log filter-bank energies (FBEs) followed by a time-domain transformation. In the past, these two transformations are usually estimated or optimized separately. In this paper, we consider sequences of log FBEs as a set of spectrogram images, and investigate an image compression technique to jointly optimize the two transformations so that the reconstruction error of the spectrogram images is minimized; there is an efficient algorithm that solves the optimization problem. The framework allows extension to other optimization costs as well.
With the advance in semiconductor memory and the availability of very large speech corpora (of hundreds to thousands of hours of speech), we would like to revisit the use of discrete hidden Markov model (DHMM) in automatic speech recognition. To estimate the discrete density in a DHMM state, the acoustic space is divided into bins and one simply count the relative amount of observations falling into each bin. With a very large speech corpus, we believe that the number of bins may be greatly increased to get a much higher density than before, and we will call the new models, the high-density discrete hidden Markov model (HDDHMM). Our HDDHMM is different from traditional DHMM in two aspects: firstly, the codebook will have a size in thousands or even tens of thousands; secondly, we propose a method based on scalar quantization indexing so that for a d-dimensional acoustic vector, the discrete codeword can be determined in O(d) time. During recognition, the state probability is reduced to an O(1) table look-up. The new HDDHMM was tested on WSJ0 with 5K vocabulary. Compared with a baseline 4-stream continuous density HMM system which has a WER of 9.71%, a 4-stream HDDHMM system converted from the former achieves a WER of 11.60%, with no distance or Gaussian computation.
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