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3.6.3 Frame Level Phone Recognition
The solutions of the cost-fidelity were used as feature representations for frame level
phone classification. In particular, we evaluated solutions associated with the following
dimensions: 4, 7, 10, 13, 19, 25, 31, 37, 43, 49, 55 and 61. GMMs were used for esti-mating
class-conditional densities in the Bayes decision setting, which is the standard
parametric model adopted for this type of frame level phone classification [9], and a
ten-fold cross-validation was used for performance evaluation. 32 mixture components
per class were considered and the EM-algorithm was used for ML parameter estima-tion.
As a reference, we consider the standard 13-dimensional Mel-Cepstrum (MFCCs)
plus delta and acceleration coefficients using the same frame rate (10ms) and window
length (64ms) — 39-feature vector associated with a total window length of 100ms,
where the correct phone classification rate (mean and standard deviation) obtained was
53.01%(1.01). The performances for the minimum cost tree pruning family using as
fidelity the proposed non-parametric CMI, as well as the energy, considered in [7], are
reported in Table 3.1. Table 3.1 also reports performances of two widely used dimen-sionality
reduction techniques acting on the raw time domain data: linear discriminant
analysis (LDA) and non-parametric discriminant analysis (NDA).
LDA and NDA present relatively poor performances compared to using filter bank
representations of the acoustic process. This can be attributed to two reasons: first these
methods are constrained to the family of linear transformations on the raw data, and
second, there is an implicit Gaussianity assumption in considering the between-within
class scatter matrices ratio as the optimality criterion on both techniques [53, 63], which
is not guaranteed to be valid in this particular high dimensional setting. When comparing
solutions of the minimum cost tree pruning using the proposed empirical MI and energy
as the fidelity criterion, the former as expected shows consistently better performance,
demonstrating the effectiveness of the empirical MI as an indicator of discrimination
66
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 79 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 3.6.3 Frame Level Phone Recognition The solutions of the cost-fidelity were used as feature representations for frame level phone classification. In particular, we evaluated solutions associated with the following dimensions: 4, 7, 10, 13, 19, 25, 31, 37, 43, 49, 55 and 61. GMMs were used for esti-mating class-conditional densities in the Bayes decision setting, which is the standard parametric model adopted for this type of frame level phone classification [9], and a ten-fold cross-validation was used for performance evaluation. 32 mixture components per class were considered and the EM-algorithm was used for ML parameter estima-tion. As a reference, we consider the standard 13-dimensional Mel-Cepstrum (MFCCs) plus delta and acceleration coefficients using the same frame rate (10ms) and window length (64ms) — 39-feature vector associated with a total window length of 100ms, where the correct phone classification rate (mean and standard deviation) obtained was 53.01%(1.01). The performances for the minimum cost tree pruning family using as fidelity the proposed non-parametric CMI, as well as the energy, considered in [7], are reported in Table 3.1. Table 3.1 also reports performances of two widely used dimen-sionality reduction techniques acting on the raw time domain data: linear discriminant analysis (LDA) and non-parametric discriminant analysis (NDA). LDA and NDA present relatively poor performances compared to using filter bank representations of the acoustic process. This can be attributed to two reasons: first these methods are constrained to the family of linear transformations on the raw data, and second, there is an implicit Gaussianity assumption in considering the between-within class scatter matrices ratio as the optimality criterion on both techniques [53, 63], which is not guaranteed to be valid in this particular high dimensional setting. When comparing solutions of the minimum cost tree pruning using the proposed empirical MI and energy as the fidelity criterion, the former as expected shows consistently better performance, demonstrating the effectiveness of the empirical MI as an indicator of discrimination 66 |
