/* -*- c-basic-offset: 4; indent-tabs-mode: nil -*- */ /* ==================================================================== * Copyright (c) 1999-2004 Carnegie Mellon University. All rights * reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * 1. Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * 2. Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in * the documentation and/or other materials provided with the * 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 * ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, * THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CARNEGIE MELLON UNIVERSITY * NOR ITS EMPLOYEES BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * * ==================================================================== * */ /* * cont_mgau.h -- Mixture Gaussians for continuous HMM models. * * ********************************************** * CMU ARPA Speech Project * * Copyright (c) 1997 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 1.15 2006/02/22 16:32:40 arthchan2003 * Merged from SPHINX3_5_2_RCI_IRII_BRANCH: 1, Dave's change in 1.18 for removing temp_hack is followed 2, The logic of uninit_compact is changed, by default the code will remove a Gaussian if it has zero mean AND zero variance. The old behavior (removal if Gaussian has zero mean.) could be retained if specifying -remove_zero_var_gau=1, 3, Fix issue in . * * * Revision 1.13.4.3 2005/07/05 21:28:57 arthchan2003 * 1, Merged from HEAD. 2, Remove redundant keyword in cont_mgau. * * Revision 1.13.4.2 2005/07/05 06:48:54 arthchan2003 * Merged from HEAD. * * Revision 1.14 2005/07/04 20:57:53 dhdfu * Finally remove the "temporary hack" for the endpointer, and do * everything in logs3 domain. Should make it faster and less likely to * crash on Alphas. * * Actually it kind of duplicates the existing GMM computation functions, * but it is slightly different (see the comment in classify.c). I don't * know the rationale for this. * * Revision 1.13.4.1 2005/07/05 05:47:59 arthchan2003 * Fixed dox-doc. struct level of documentation are included. * * * Revision 1.13 2005/06/21 18:06:45 arthchan2003 * * Log. 1, Fixed Doxygen documentation. 2, Added $Log$ * Revision 1.1 2006/04/05 20:27:30 dhdfu * A Great Reorganzation of header files and executables * * Log. 1, Fixed Doxygen documentation. 2, Added Revision 1.15 2006/02/22 16:32:40 arthchan2003 * Log. 1, Fixed Doxygen documentation. 2, Added Merged from SPHINX3_5_2_RCI_IRII_BRANCH: 1, Dave's change in 1.18 for removing temp_hack is followed 2, The logic of uninit_compact is changed, by default the code will remove a Gaussian if it has zero mean AND zero variance. The old behavior (removal if Gaussian has zero mean.) could be retained if specifying -remove_zero_var_gau=1, 3, Fix issue in . * Log. 1, Fixed Doxygen documentation. 2, Added * * Revision 1.4 2005/06/13 04:02:55 archan * Fixed most doxygen-style documentation under libs3decoder. * * Revision 1.3 2005/03/30 01:22:46 archan * Fixed mistakes in last updates. Add * * * 20.Apr.2001 RAH (rhoughton@mediasite.com, ricky.houghton@cs.cmu.edu) * Added mgau_free to free memory allocated by mgau_init() * 15-Dec-1999 M K Ravishankar (rkm@cs.cmu.edu) at Carnegie Mellon University * Added mgau_model_t.{frm_sen_eval,frm_gau_eval}. * Added mgau_var_nzvec_floor(). * * 28-Mar-1999 M K Ravishankar (rkm@cs.cmu.edu) at Carnegie Mellon University. * Started. */ #ifndef _S3_CONT_MGAU_H_ #define _S3_CONT_MGAU_H_ #include /** \file cont_mgau.h * \brief Interface of full GMM computation with integer value of log likelihood. */ #ifdef __cplusplus extern "C" { #endif #if 0 } /* Fool Emacs into not indenting things. */ #endif /** * Specification of sets of GMM */ #define CONTHMM 10001 /**(NOT USED, for backward compatibility only) */ #define SEMIHMM 10002 /**(NOT USED, for backward compatibllity only) */ #define FULL_INT_COMP 20001 /** (NOT USED) Use full integer computation */ #define FULL_FLOAT_COMP 20002 /** (NOT USED) Use full floating point computation */ #define MIX_INT_FLOAT_COMP 20003 /** (NOT USED) Use mixture of integer and floating point computation */ #define MGAU_MEAN 1 /** Constant specifying the mean is used */ #define MGAU_VAR 2 /** Constant specifying the variance is used */ #define MGAU_FULLVAR 3 /** Constant specifying the full covariance is used */ #define NO_BSTIDX -1 /** When there is no best index */ #define NOT_UPDATED -100 /** Constant defined that a gaussian is not updated. */ /** * Mixture Gaussians: Weighted set of Gaussian densities, each with its own mean vector and * diagonal covariance matrix. Specialized for continuous HMMs to improve speed performance. * So, a separate mixture Gaussian, with its own mixture weights, for each HMM state. Also, * a single feature stream assumed. (In other words, the mgau_t structure below represents * a senone in a fully continuous HMM model.) * * Given a Gaussian density with mean vector m and diagonal variance vector v, and some * input vector x, all of length n, the Mahalanobis distance of x from the Gaussian mean m * is given by: * {1/sqrt((2pi)^n * det(v))} * exp{-Sum((x[i] - m[i])^2 / (2v[i]))} * To speed up this evaluation, the first sub-expression ({1/sqrt...}) can be precomputed at * initialization, and so can 1/2v[i] in the second sub-expression. Secondly, recognition * systems work with log-likelihood values, so these distances or likelihood values are * computed in log-domain. Finally, float32 operations are costlier than int32 ones, so * the log-values are converted to logs3 domain (see libmisc/logs3.h) (but before the mixing * weights are applied). Thus, to reiterate, the final scores are (int32) logs3 values. */ /* 20040826 ARCHAN: * Introduced hook to the GMM definition to allow gaussian computation using full float * operations. Also added another hook that allows potential use of full covariance matrix. * At this point, full covariance matrix computation was not fully implemented. * If comp_type = MIX_INT_FLOAT_COMP, then the interger pointer will be used to store the * mixture weight. It is also important to initialize the logs3 routine separately. * If comp_type = FULL_FLOAT_COMP, then the floating point pointer will be used to store the mixture * weights. */ /** * \struct mgau_t * \brief A single mixture-Gaussian model for one senone (see above comment). */ typedef struct { int32 n_comp; /**< #Component Gaussians in this mixture. NOTE: May be 0 (for the untrained states). */ int32 bstidx; /**< Index for the most likely Gaussians in this mixture of component. It persists through time. */ int32 bstscr; /**< Scores for the most likely Gaussians in this mixture of component.*/ int32 updatetime; /**< Update time */ /* Definition for mean */ float32 **mean; /**< The n_comp means of the Gaussians. The mean vector for a single mixture-Gaussian model for one senone. Dimension: n_comp * dimension */ /* Definition for variances */ float32 **var; /**< The n_comp (diagonal) variances of the Gaussians. Could be converted to 1/(2*var) for faster computation (see above comment). The diagonal variance vector for a single mixture-Gaussian model for one senone. Dimension: n_comp * dimension */ float32 ***fullvar; /* The n_comp (full) variances of the Gaussians. */ /* A full co-variance matrix for a single mixture-Gaussian model for one senone */ /* Dimension: n_comp * dimension * dimension */ /* Definition for the log reciprocal terms */ float32 *lrd; /**< Log(Reciprocal(Determinant (variance))). (Then there is also a (2pi)^(veclen) involved...) */ /* Definitions for the mixture weights */ int32 *mixw; /**< Mixture weights for the n_comp components (int32 instead of float32 because these values are in logs3 domain)*/ float32 *mixw_f; /**< (ONLY USED IN ENDPOINTER) mixture weights for the n_comp components in float32 */ } mgau_t; /** * \struct mgau_model_t * \brief The set of mixture-Gaussians in an acoustic model used in Sphinx 3.X family of tool */ typedef struct { int32 n_mgau; /**< #Mixture Gaussians in this model (i.e., #senones) */ int32 max_comp; /**< Max components in any mixture */ int32 veclen; /**< Vector length of the Gaussian density means (and diagonal vars) */ mgau_t *mgau; /**< The n_mgau mixture Gaussians */ float64 distfloor; /**< Mahalanobis distances can underflow when finally converted to logs3 values. To prevent this, floor the log values first. */ int32 comp_type; /**< Type of computation used in this set of mixture-Gaussians*/ int32 verbose; /**< Whether to display information */ /* Used only in the flat lexicon decoder, statistics */ int32 frm_sen_eval; /**< #Senones evaluated in the most recent frame */ int32 frm_gau_eval; /**< #Gaussian densities evaluated in the most recent frame */ int32 frm_ci_sen_eval; /**< #CI Senones evaluated in most recent frame*/ int32 frm_ci_gau_eval; /**< #CI Senones evaluated in most recent frame*/ int32 gau_type; /**< gau_type=CONTHMM if it is fully continous HMM, gau_type=SEMIHMM if it is semi continous HMM. Currently SEMIHMM is not supported. */ } mgau_model_t; /** Access macros */ /** \def mgau_n_mgau Access number of GMMs \def mgau_max_comp Access the maximum number of components. It can be different across GMMs \def mgau_veclen Access an integer array contains the size of each stream \def mgau_n_comp Access the number of component for a particular mixture in a GMM \def mgau_mean Access the mean \def mgau_var Access the variance \def mgau_lrd Access the floating point version of the Gaussian constant \def mgau_lrd Access the integer version of the Gaussian constant \def mgau_frm_sen_eval Number of senones evaluated in this frame \def mgau_frm_gau_eval Number of gaussians evaluated in this frame \def mgau_frm_ci_sen_eval Number of CI senones evaluated in this frame \def mgau_frm_ci_gau_eval Number of CI gaussians evaluated in this frame */ #define mgau_n_mgau(g) ((g)->n_mgau) #define mgau_max_comp(g) ((g)->max_comp) #define mgau_veclen(g) ((g)->veclen) #define mgau_n_comp(g,m) ((g)->mgau[m].n_comp) #define mgau_mean(g,m,c) ((g)->mgau[m].mean[c]) #define mgau_var(g,m,c) ((g)->mgau[m].var[c]) #define mgau_fullvar(g,m,c) ((g)->mgau[m].fullvar[c]) #define mgau_mixw(g,m,c) ((g)->mgau[m].mixw[c]) #define mgau_mixw_f(g,m,c) ((g)->mgau[m].mixw_f[c]) #define mgau_lrd(g,m,c) ((g)->mgau[m].lrd[c]) #define mgau_lrdi(g,m,c) ((g)->mgau[m].lrdi[c]) #define mgau_frm_sen_eval(g) ((g)->frm_sen_eval) #define mgau_frm_gau_eval(g) ((g)->frm_gau_eval) #define mgau_frm_cisen_eval(g) ((g)->frm_ci_sen_eval) #define mgau_frm_cigau_eval(g) ((g)->frm_ci_gau_eval) /** * Create a new mixture Gaussian model from the given files (Sphinx3 format). Optionally, * apply the precomputations mentioned in the main comment above. * @see mgau_file_read * @see mgau_mixw_read * @see mgau_uninit_compact * @see mgau_var_floor * @see mgau_precomp * @return pointer to the model created if successful; NULL if error. */ S3DECODER_EXPORT mgau_model_t * mgau_init (char *meanfile, /**< In: File containing means of mixture gaussians */ char *varfile, /**< In: File containing variances of mixture gaussians */ float64 varfloor, /**< In: Floor value applied to variances; e.g., 0.0001 */ char *mixwfile, /**< In: File containing mixture weights */ float64 mixwfloor, /**< In: Floor value for mixture weights; e.g., 0.0000001 */ int32 precomp, /**< In: If TRUE, create and precompute mgau_t.lrd and also transform each var value to 1/(2*var). (If FALSE, one cannot use the evaluation routines provided here.) */ char* senmgau, /**< In: type of the gaussians distribution, .cont. or .semi. FIX me! This is confusing!*/ int32 comp_type); /**< In: Type of computation in this set of gaussian mixtures. */ /** * Floor any variance vector that is non-zero (vector). * @return No. of variance VALUES floored. */ int32 mgau_var_nzvec_floor (mgau_model_t *g, /**< In: A mixture of Gaussian components */ float64 floor /**< In: The floor value */ ); /** * Evaluate a single mixture Gaussian at the given vector x; i.e., compute the Mahalanobis * distance of x from each mean in the mixture, and combine them using the mixture weights. * Return value: The final score from this evaluation (a logs3 domain value). NOTE: if the * specified mixture is empty, S3_LOGPROB_ZERO is returned (see libmisc/libmisc.h). * @return the senone score. */ /* The hybrid integer and floating point implementation of GMM computation */ int32 mgau_eval (mgau_model_t *g, /**< In: The entire mixture Gaussian model */ int32 m, /**< In: The chosen mixture in the model (i.e., g->mgau[m]) */ int32 *active_comp, /**< In: An optional, -1 terminated list of active component indices; if non-NULL, only the specified components are used in the evaluation. */ float32 *x, /**< In: Input observation vector (of length g->veclen). */ int32 fr, /**< In: Frame number where GMM m is updated */ int32 bUpdBstIdx /**< In: Whether the best index for the GMM will be updated or not */ ); /** * Like mgau_eval, but return the scores of the individual components, instead of combining * them into a senone score. * @return: Best component score. */ int32 mgau_comp_eval (mgau_model_t *g, /**< In: Set of mixture Gaussians */ int32 m, /**< In: Mixture being considered */ float32 *x, /**< In: Input vector being compared to the components */ int32 *score); /**< Out: Array of scores for each component */ /** * A routine that dump all mean and variance parameters of a set of gaussian distribution. * @return always 0 */ int32 mgau_dump (mgau_model_t *g, /**< In: Set of mixture Gaussians */ int32 type /**< In: type of output, MGAU_MEAN for mean or MGAU_VAR for variance. */ ); /** RAH * Free memory allocated by mgau_init */ S3DECODER_EXPORT void mgau_free (mgau_model_t *g /**< In: A set of model to free */ ); /** * Reloading the means. This is particularly useful for speaker adaptation. */ S3DECODER_EXPORT int32 mgau_mean_reload(mgau_model_t *g, /**< In/Out : The mean which will be resetted*/ char* mean_file_name /**< In: The mean files */ ); #if 0 { /* Stop indent from complaining */ #endif #ifdef __cplusplus } #endif #endif