Page 76 |
Save page Remove page | Previous | 76 of 196 | Next |
|
small (250x250 max)
medium (500x500 max)
Large (1000x1000 max)
Extra Large
large ( > 500x500)
Full Resolution
All (PDF)
|
This page
All
|
2) Use Darbellay-Vajda algorithm to construct partition QN3
for X3 using
{xi
3, yi : i = 1, ..,N}.
3) Consider the product adaptive partition QN1
,2×3 to:
3.1) Compute empirical joint distribution ˆ PN
X1,X2,X3,Y for every event in
(QN1
,2×3) × FY.
3.2) Compute empirical MI indicators ˆI
QN 1,2×3
N (X1,X2,X3; Y ) and ˆIQN3
N (X3; Y ).
3.3) Finally compute the CMI estimate ˆ N(QN1
,2×3).
3.6 Experiments
In this section we report experiments to evaluate: the non-parametric CMI estimator
across the different scale-frequency values of the WP basis family; the solutions the
minimum cost tree pruning in terms of the expected frequency band decompositions,
and the classification performance of the resulting feature descriptions in comparison
with some standard feature representations.
3.6.1 Frame Level Phone Classification from Speech Signal
We consider an automatic speech recognition scenario, where filter banks have been
widely used for feature representations and, furthermore, concrete ideas for the opti-mal
frequency band decompositions are well understood based on perceptual studies
of the human auditory system. The corpus used was collected in our group at USC
and comprises about 1 hour 30 minutes of spontaneous conversational speech from a
male English speaker, sampled at 16 Khz. The standard frame by frame analysis was
63
Object Description
| Title | On optimal signal representation for statistical learning and pattern recognition |
| Author | Silva, Jorge |
| Author email | jorgesil@usc.edu; josilva@ing.uchile.cl |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2008-06-23 |
| Date submitted | 2008 |
| Restricted until | Unrestricted |
| Date published | 2008-10-21 |
| Advisor (committee chair) | Narayanan, Shrikanth S. |
| Advisor (committee member) |
Kuo, C.-C. Jay Ordóñez, Fernando I. |
| Abstract | This work presents contributions on two important aspects of the role of signal representation in statistical learning problems, in the context of deriving new methodologies and representations for speech recognition and the estimation of information theoretic quantities.; The first topic focuses on the problem of optimal filter bank selection using Wavelet Packets (WPs) for speech recognition applications. We propose new results to show an estimation-approximation error tradeoff across sequence of embedded representations. These results were used to formulate the minimum probability of error signal representation (MPE-SR) problem as a complexity regularization criterion. Restricting this criterion to the filter bank selection, algorithmic solutions are provided by exploring the dyadic tree-structure of WPs. These solutions are stipulated in terms of a set of conditional independent assumptions for the acoustic observation process, in particular, a Markov tree property across the indexed structure of WPs. In the technical side, this work presents contributions on the extension of minimum cost tree pruning algorithms and their properties to affine tree functionals. For the experimental validation, a phone classification task ratifies the goodness of Wavelet Packets as an analysis scheme for non-stationary time-series processes, and the effectiveness of the MPE-SR to provide cost effective discriminative filter bank solution for pattern recognition.; The second topic addresses the problem of data-dependent partitions for the estimation of mutual information and Kullback-Leibler divergence (KLD). This work proposes general histogram-based estimates considering non-product data-driven partition schemes. The main contribution is the stipulation of sufficient conditions to make these histogram-based constructions strongly consistent for both problems. The sufficient conditions consider combinatorial complexity indicator for partition families and the use of large deviation type of inequalities (Vapnik-Chervonenkis inequalities). On the application side, two emblematic data-dependent constructions are derived from this result, one based on statistically equivalent blocks and the other, on a tree-structured vector quantization scheme. A range of design values was stipulated to guarantee strongly consistency estimates for both framework. Furthermore, experimental results under controlled settings demonstrate the superiority of these data-driven techniques in terms of a bias-variance analysis when compared to conventional product histogram-based and kernel plug-in estimates. |
| Keyword | signal representation in statistical learning; Bayes decision theory; basis selection; tree-structured bases and Wavelet packet (WP); complexity regularization; minimum cost tree pruning; family pruning problem; mutual information estimation; divergence estimation; data-dependent partitions; statistical learning theory; concentration inequalities; tree-structured vector quantization. |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m1684 |
| Contributing entity | University of Southern California |
| Rights | Silva, Jorge |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Silva-2450 |
| Archival file | uscthesesreloadpub_Volume32/etd-Silva-2450.pdf |
Description
| Title | Page 76 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 2) Use Darbellay-Vajda algorithm to construct partition QN3 for X3 using {xi 3, yi : i = 1, ..,N}. 3) Consider the product adaptive partition QN1 ,2×3 to: 3.1) Compute empirical joint distribution ˆ PN X1,X2,X3,Y for every event in (QN1 ,2×3) × FY. 3.2) Compute empirical MI indicators ˆI QN 1,2×3 N (X1,X2,X3; Y ) and ˆIQN3 N (X3; Y ). 3.3) Finally compute the CMI estimate ˆ N(QN1 ,2×3). 3.6 Experiments In this section we report experiments to evaluate: the non-parametric CMI estimator across the different scale-frequency values of the WP basis family; the solutions the minimum cost tree pruning in terms of the expected frequency band decompositions, and the classification performance of the resulting feature descriptions in comparison with some standard feature representations. 3.6.1 Frame Level Phone Classification from Speech Signal We consider an automatic speech recognition scenario, where filter banks have been widely used for feature representations and, furthermore, concrete ideas for the opti-mal frequency band decompositions are well understood based on perceptual studies of the human auditory system. The corpus used was collected in our group at USC and comprises about 1 hour 30 minutes of spontaneous conversational speech from a male English speaker, sampled at 16 Khz. The standard frame by frame analysis was 63 |
