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Recently, we proposed an i-vector approach to acoustic sniffing for irrelevant variability normalization based acoustic model training in large vocabulary continuous speech recognition (LVCSR). Its effectiveness has been confirmed by experimental results on Switchboard- 1 conversational telephone speech transcription task. In this paper, we study several discriminative feature extraction approaches...
We propose a new component-tree based method with efficient and effective pruning strategies for userintention guided text extraction from scene images. A grayscale image is represented first as two component-trees, whose nodes represent possible candidates of character components. The non-text candidates are then pruned by using contrast, geometric and text line information as well as the constraint...
This paper presents a discriminative training (DT) approach to irrelevant variability normalization (IVN) based training of feature transforms and hidden Markov models for large vocabulary continuous speech recognition. A speaker-clustering based method is used for acoustic sniffing and maximum mutual information (MMI) is used as a training criterion. Combined with unsupervised adaptation of feature...
We present a study of discriminative training of classifiers using both maximum mutual information (MMI) and minimum classification error (MCE) criteria for online handwritten Chinese/Japanese character recognition based on continuous-density hidden Markov models. It is observed that MCE-trained classifiers can achieve a much higher recognition accuracy than that of MMI-trained ones. Benchmark results...
We present a study of designing compact recognizers of handwritten Chinese characters using multiple-prototype based classifiers. A modified Quick prop algorithm is proposed to optimize a sample-separation-margin based minimum classification error objective function. Split vector quantization technique is used to compress classifier parameters. Benchmark results are reported for classifiers with different...
In this paper, we present a new approach to minimum classification error (MCE) training of pattern classifiers with quadratic discriminant functions. First, a so-called sample separation margin (SSM) is defined for each training sample and then used to define the misclassification measure in MCE formulation. The computation of SSM can be cast as a nonlinear constrained optimization problem and solved...
This paper presents a character-structure-guided approach to estimating possible orientations of a rotated isolated online handwritten Chinese character. Using the estimated orientations, the original distorted sample can be transformed to a normal position, which can be recognized more accurately by using a classifier trained from normal-position samples. The effectiveness of this approach is demonstrated...
In our previous work, a precision constrained Gaussian model (PCGM) was proposed for character modeling to design compact recognizers of handwritten Chinese characters. A maximum likelihood training procedure was developed to estimate model parameters from training data. In this paper, we extend the above work by using minimum classification error (MCE) training to improve recognition accuracy and...
We present a new feature extraction approach to online Chinese handwriting recognition based on continuous-density hidden Markov models (CDHMM). Given an online handwriting sample, a sequence of time-ordered dominant points are extracted first, which include stroke-endings, points corresponding to local extrema of curvature, and points with a large distance to the chords formed by pairs of previously...
In our previous work, a new HMM compensation approach for static MFCC features was proposed by using a technique called Unscented Transformation (UT). Three implementations of the UT approach with different computational complexities were evaluated on Aurora2 connected digits database, and significant performance improvements were achieved compared to log-normal- approximation-based PMC (Parallel...
In this paper we propose a generalized feature transformation approach to compensating for channel variation in speaker verification (SV) applications. Channel-dependent (CD) piecewise linear transformations are used for feature compensation. CD transformation parameters are estimated together with a channel-independent (CI) root Gaussian mixture model (GMM) from training data with a variety of channel...
Nonlinear shape normalization (NSN) approaches based on line density equalization have been the most popular choice for both offline and online handwritten Chinese character recognition (HCCR). However, in a recent study of using 8-directional features for online HCCR, we discovered that an NSN approach based on dot density equalization achieved a much better performance than that of an NSN approach...
We've been developing a Chinese OCR engine for handwritten Chinese scripts. Currently, our OCR engine supports a vocabulary of 4616 characters which include 4516 simplified Chinese characters in GB2312-80, 62 alphanumeric characters, 38 punctuation marks and symbols. By using 1,384,800 character samples to train our recognizer, an averaged character recognition accuracy of 96.34% is achieved on a...
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