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/* ====================================================================
* Copyright (c) 1999-2004 Carnegie Mellon University. All rights
* reserved.
*
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* 2. Redistributions in binary form must reproduce the above copyright
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* distribution.
*
* This work was supported in part by funding from the Defense Advanced
* Research Projects Agency and the National Science Foundation of the
* United States of America, and the CMU Sphinx Speech Consortium.
*
* THIS SOFTWARE IS PROVIDED BY CARNEGIE MELLON UNIVERSITY ``AS IS'' AND
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/*
* approx_cont_mgau.h
*
* **********************************************
* CMU ARPA Speech Project
*
* Copyright (c) 1999 Carnegie Mellon University.
* ALL RIGHTS RESERVED.
* **********************************************
*
* HISTORY
* $Log$
* Revision 1.1 2006/04/05 20:27:30 dhdfu
* A Great Reorganzation of header files and executables
*
* Revision 1.10 2005/06/21 18:05:12 arthchan2003
* Log. approx_cont_mgau_frame_eval has interface's changes. ci_senscr,
* best_score is now wrapped up ascr_t. approx_cont_mgau_ci_eval is now
* taking care of maxing ci senone score for a frame.
*
* Revision 1.6 2005/06/09 20:55:29 archan
* Add comments for approx_cont_mgau.h.
*
* Revision 1.5 2005/04/20 03:30:58 archan
* Part of refactoring: move best scores inside approx_cont_mgau.h
*
* Revision 1.4 2005/03/30 01:22:46 archan
* Fixed mistakes in last updates. Add
*
*
* 23-Jan-2004 Arthur Chan (archan@cs.cmu.edu)
* started
*/
#ifndef _S3_APPROXCONGAU_H_
#define _S3_APPROXCONGAU_H_
#include <logmath.h>
#include <profile.h>
#include "cont_mgau.h"
#include "subvq.h"
#include "gs.h"
#include "fast_algo_struct.h"
#include "ascr.h"
#include "mdef.h"
#include "s3types.h"
#ifdef __cplusplus
extern "C" {
#endif
#if 0
} /* Fool Emacs into not indenting things. */
#endif
/** \file approx_cont_mgau.h
* \brief Master function to compute the approximate score of mixture of Gaussians
\warning You need to have some knowledge in fast GMM computation in order to modifed this function.
This is the current schemes included:
1, VQ-based Gaussian Selection
2, Subvq-based Gaussian Selection
3, Context Independent Phone-based GMM Selection
4, Down Sampling
a, dumb approach,
b, conditional down sampling (currently can only be used with VQ-based Gaussian Selection
c, distance-based down sampling
The above method of categorizing GMM computation in 4 levels are
presented in ICSLP 2004. For the publication, please visit
Arthur Chan's web site at www.cs.cmu.edu/~archan/ .
*/
/**
approx_con_mgau_frame_eval encapsulates all approximations in the
Gaussian computation. This assumes programmers NOT to initialize
the senone scores at every frame before using this function. This
modularize this routine but complicated issues such as
frame-dropping which can also be done in the front-end
This layer of code controls the optimization performance in Frame
Leval and GMM Level.
Frame Level:
^^^^^^^^^^^^
We select to compute the scores only if it is not similar to the
most recently computed frames. There are multiple ways to
configures this.
Naive down-sampling : Skip the computation one every other n-frames
Conditional down-sampling : Skip the computation only if the
current frame doesn't belong to the same neighborhood of the same
frame. This neighborhood corresponds to the codeword which the
feature vector found to be the closest.
No matter which down-sampling was used, the following problem will
appear in the computation. Active senones of frame which supposed
to be skipped in computation could be not computed in the most
recently computed frame. In those cases, we chose to compute those
senones completely.
GMM Level:
^^^^^^^^^^
In the implementation of CI-based GMM selection makes use of the
fact that in s3.3 , CI models are always placed before all CD
models. Hence the following logic is implemented:
if(it is CI senone)
compute score
else if (it is CD senone)
if the ci-phone beam was not set
compute score
else
if the CD senone's parent has a score within the beam
compute_score
else CD senone's parent has a score out of the beam
back-off using the parent senone score.
During s3.5, the idea of bestidx in a GMM has been changed and
the above logic becomes
if(it is CI senone)
compute score
else if (it is CD senone)
if the ci-phone beam was not set
compute score
else
if the CD senone's parent has a score within the beam
compute_score
else CD senone's parent has a score out of the beam
if the bestindex of the last frame exists
compute score using the bestidx
then
back-off using the parent senone score.
About renormalization
^^^^^^^^^^^^^^^^^^^^^
Sphinx 3.4 generally renormalize the score using the best
score. Notice that this introduce extra complication to the
implementation. I have separated the logic of computing or not
computing the scores. This will clarify the code a bit.
Accounting of senone and gaussian computation
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This function assumes approx_cont_mgau_ci_eval was run before it,
hence at the end the score was added on top of the it.
Design
^^^^^^
The whole idea of this function is based on my paper on "4-level
categorization of GMM computation " which basically describe how
different techniques of fast GMM computation should interact with
each others. The current implementation was there to make the
code to be as short as possible. I hope that no one will try to make
the code to be longer than 500 lines.
Imperfection
^^^^^^^^^^^^
Imperfections of the code can be easily seen by experts so I want
to point out before they freak out. There are synchronization
mechanism in the bestindex and rec_sen_active. That can easily be a
source of error. I didn't do it because somehow when you trust
just the best matching index of the previous frame is slightly
different from if you trust the score of the previous frame
The sen_active, rec_sen_active and senscr should be inside the GMM
structure rather than just a separate array. I didn't fix it
because this change will also touch other data structures as well.
@see approx_mgau_eval
@return the best senone score
*/
S3DECODER_EXPORT
int32 approx_cont_mgau_frame_eval (mdef_t *mdef,
subvq_t *svq,
gs_t *gs, /**< Input mdef, svq and gs*/
mgau_model_t *g,
fast_gmm_t *fastgmm, /**< Input/Output: wrapper for
parameters for Fast GMM , for
all beams and parameters, during
the computation, the */
ascr_t *a, /**< Input/Output: wrapper for all acoustic scores
arrays */
float32 *feat, /**< Input: the current feature vector */
int32 frame, /**< Input: The frame number */
int32 *cache_ci_senscr, /**< Input: The cache CI scores for this frame */
ptmr_t *tm_ovrhd, /**< Output: the timer used for computing overhead */
logmath_t *logmath
);
/**
* Evaluate the approximate gaussian score for CI senone for one
* frame. In Sphinx 3.X (X=4,5), this routine is used to precompute
* CI senone score as a kind of approximate match of the CD score.
*/
S3DECODER_EXPORT
void approx_cont_mgau_ci_eval (
subvq_t *svq,
gs_t *gs,
mgau_model_t *g,
fast_gmm_t *fg, /**< Input/Output: wrapper for
parameters for Fast GMM , for
all beams and parameters, during
the computation, the */
mdef_t *mdef, /**< Input : model definition */
float32 *feat, /**< Input : the current frame of feature */
int32 *ci_senscr, /** Input/Output : ci senone score, a one dimension array */
int32 *best_score, /** Input/Output: the best score, a scalar */
int32 fr, /** In : The frame number */
logmath_t *logmath
);
#if 0
{ /* Stop indent from complaining */
#endif
#ifdef __cplusplus
}
#endif
#endif
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