/* -*- 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. * * ==================================================================== * */ /* * subvq.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 1.14 2006/02/22 17:43:32 arthchan2003 * Merged from SPHINX3_5_2_RCI_IRII_BRANCH: * 1, vector_gautbl_free is not appropiate to be used in this case because it will free a certain piece of memory twice. * 2, Fixed dox-doc. * * 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 19:01:33 arthchan2003 * Added $ keyword. * * Revision 1.3 2005/03/30 01:22:47 archan * Fixed mistakes in last updates. Add * * * 20.Apr.2001 RAH (rhoughton@mediasite.com, ricky.houghton@cs.cmu.edu) * Updated subvq_free () to free more allocated memory * * 15-Dec-1999 M K Ravishankar (rkm@cs.cmu.edu) at Carnegie Mellon University * Moved subvq_t.{frm_sen_eval,frm_gau_eval} to cont_mgau.h. * * 14-Dec-1999 M K Ravishankar (rkm@cs.cmu.edu) at Carnegie Mellon University * Added subvq_t.{frm_sen_eval,frm_gau_eval}. Changed subvq_frame_eval to * return the normalization factor. * * 06-Dec-1999 M K Ravishankar (rkm@cs.cmu.edu) at Carnegie Mellon University * Added subvq_subvec_eval_logs3(). * * 14-Oct-1999 M K Ravishankar (rkm@cs.cmu.edu) at Carnegie Mellon University * Changed ci_active flags input to sen_active in subvq_frame_eval(). * * 20-Jul-1999 M K Ravishankar (rkm@cs.cmu.edu) at Carnegie Mellon University * Added subvq_gautbl_eval_logs3(). * * 12-Mar-1999 M K Ravishankar (rkm@cs.cmu.edu) at Carnegie Mellon University * Started. */ #ifndef _S3_SUBVQ_H_ #define _S3_SUBVQ_H_ #include #include "s3types.h" #include "cont_mgau.h" #include "vector.h" #include "logs3.h" /** \file subvq.h \brief Implementation of Sub-vector quantization. */ #ifdef __cplusplus extern "C" { #endif #if 0 } /* Fool Emacs into not indenting things. */ #endif /** \struct subvq_t * \brief Wrapper structures of sub-vector quantization */ typedef struct { arraysize_t origsize; /**< origsize.r = #codebooks (or states) in original model; origsize.c = max #codewords/codebook in original model. */ int32 n_sv; /**< #Subvectors */ int32 vqsize; /**< #Codewords in each subvector quantized mean/var table */ int32 **featdim; /**< featdim[s] = Original feature dimensions in subvector s */ vector_gautbl_t *gautbl; /**< Vector-quantized Gaussians table for each sub-vector */ int32 ***map; /**< map[i][j] = map from original codebook(i)/codeword(j) to sequence of nearest vector quantized subvector codewords; so, each map[i][j] is of length n_sv. Finally, map is LINEARIZED, so that it indexes into a 1-D array of scores rather than a 2-D array (for faster access). */ /* Working space used during evaluation. */ float32 *subvec; /**< Subvector extracted from feature vector */ int32 **vqdist; /**< vqdist[i][j] = score (distance) for i-th subvector compared to j-th subvector-codeword */ int32 *gauscore; /**< Subvq-based approx. Gaussian density scores for one mixture */ int32 *mgau_sl; /**< Shortlist for one mixture (based on gauscore[]) */ /* ARCHAN, 1111, 04, move the static global variables to the structure again. */ /* RAH, 5.8.01, VQ_EVAL determines how many vectors are used to * compute the shortlist, for now this value is only relevant when n_sv =3. * Setting it to 1 means that only the CEP values are estimated, 2 means that * CEP and delta values are estimated, 3 means all three are estimated. * Note, we must adjust the beam widths as we muck around with these. */ int32 VQ_EVAL; /** Number of sub-vector to be computed */ } subvq_t; /** * SubVQ file format: * VQParam #Original-Codebooks #Original-Codewords/codebook(max) -> #Subvectors #VQ-codewords * Subvector 0 length ... * Subvector 1 length ... * ... * Codebook 0 * Row 0 of mean/var values (interleaved) for subvector 0 codebook (in 1 line) * Row 1 of above * Row 2 of above * ... * Map 0 * Mappings for state 0 codewords (in original model) to codewords of this subvector codebook * Mappings for state 1 codewords (in original model) to codewords of this subvector codebook * Mappings for state 2 codewords (in original model) to codewords of this subvector codebook * ... * Repeated for each subvector codebook 1 * Repeated for each subvector codebook 2 * ... * End * @return initialized sub-vq */ subvq_t *subvq_init (char *file, /**< In: Subvector model file */ float64 varfloor, /**< In: Floor to be applied to variance values */ int32 max_sv, /**< In: Use the first so many subvectors instead of all; if <0, use all */ mgau_model_t *g /**< In: Original model from which this subvq model was built, for cross-validation; optional */ ); /** * Re-entrant version of subvq_init(). */ subvq_t *subvq_init_r(char *file, /**< In: Subvector model file */ float64 varfloor, /**< In: Floor to be applied to variance values */ int32 max_sv, /**< In: Use the first so many subvectors instead of all; if <0, use all */ mgau_model_t *g, /**< In: Original model from which this subvq model was built, for cross-validation; optional */ cmd_ln_t *config ); /** Deallocate sub-vector quantization */ void subvq_free (subvq_t *vq /**< In: A sub-vector model */ ); /** * Evaluate senone scores for one frame. If subvq model is available, for each senone, first * get approximate Gaussian density scores using it; obtain a shortlist of Gaussians using * these scores, then evaluate the shortlist exactly. If no subvq model, evaluate senones * using all Gaussian densities. Finally, scale senone scores by subtracting the best. * @return The normalization factor (best senone absolute score). */ int32 subvq_frame_eval (subvq_t *vq, /**< In: A sub-vector model */ mgau_model_t *g,/**< In: Exact mixture Gaussian model */ int32 beam, /**< In: (Logs3) threshold for selecting shortlist; range = [-infinity(widest beam), 0(narrowest)] */ float32 *feat, /**< In: Input feature vector for this frame */ int32 *sen_active, /**< In: Active flags for each senone (optional). If not NULL, only active ones evaluated */ int32 *senscr /**< Out: Normalized senone scores */ ); /** * Evaluate the Mahalanobis distances between the given feature vector and each entry in the * given subvq codebook. Save results, as logs3 values, in vq->vqdist[][]. */ void subvq_gautbl_eval_logs3 (subvq_t *vq, /**< In/Out: Reference subvq structure */ float32 *feat /**< In: Subvectors extracted from this, and compared to relevant subvq codewords */ ); /** * Evaluate the codewords for a single given subvector sv, wrt the input feature vector. * Save results, as logs3 values, in vq->vqdist[sv][]. * (Basically, like subvq_gautbl_eval_logs3, but for a single given subvector instead of all.) */ void subvq_subvec_eval_logs3 (subvq_t *vq, /**< In/Out: Reference subvq structure */ float32 *feat, /**< In: Input feature ** subvector ** extracted from ** this, and compared ** to relevant ** codewords */ int32 sv /**< In: ID of subvector being evaluated */ ); /* * Based on previously computed subvq scores (Mahalanobis distances), determine the active * components in the given mixture (using the vq->map). * @return Number of candidates in the returned shortlist. */ int32 subvq_mgau_shortlist (subvq_t *vq, /**< In subvq */ int32 m, /**< In: GMM index */ int32 n, /**< In: #Components in specified mixture */ int32 beam /**< In: Threshold to select active components */ ); /** * Compute the scores of a gaussian using only sum of the sub-vector scores. * @return best senone score. */ int32 subvq_mgau_eval (mgau_model_t *g, /**< In: Gaussian */ subvq_t *vq, /**< In: the SVQ */ int32 m, /**< In: GMM Index */ int32 n, /**< In :#Components in a specified mixture */ int32 *active /**< In:Active list of mixture */ ); #if 0 { /* Stop indent from complaining */ #endif #ifdef __cplusplus } #endif #endif