Christian Gollan

Deformation Models for Image Recognition

We present the application of different nonlinear image deformation models to the task of image recognition. The deformation models are especially suited for local changes as they often occur in the presence of image object variability. We show that, among the discussed models, there is one approach that combines simplicity of implementation, low-computational complexity, and highly competitive performance across various real-world image recognition tasks. We show experimentally that the model performs very well for four different handwritten digit recognition tasks and for the classification of medical images, thus showing high generalization capacity. In particular, an error rate of 0.54 percent on the MNIST benchmark is achieved, as well as the lowest reported error rate, specifically 12.6 percent, in the 2005 international ImageCLEF evaluation of medical image specifically categorization.

Cross domain automatic transcription on the TC-STAR EPPS corpus

This paper describes the ongoing development of the British English European Parliament Plenary Session corpus. This corpus will be part of the speech-to-speech translation evaluation infrastructure of the European TC-STAR project. Furthermore, we present first recognition results on the English speech recordings. The transcription system has been derived from an older speech recognition system built for the North-American broadcast news task. We report on the measures taken for rapid cross-domain porting and present encouraging results.

Local context in non-linear deformation models for handwritten character recognition

We evaluate different two-dimensional non-linear deformation models for handwritten character recognition. Starting from a true two-dimensional model, we derive pseudo-two-dimensional and zero-order deformation models. Experiments show that it is most important to include suitable representations of the local image context of each pixel to increase performance. With these methods, we achieve very competitive results across five different tasks, in particular 0.5% error rate on the MNIST task.

Classification of Medical Images Using Non-linear Distortion Models

We propose the application of two-dimensional distortion models for comparisons of medical images in a distance-based classifier. We extend a simple zero-order distortion model by using local context within the compared image parts. Vertical and horizontal image gradients as well as small sub images are used as local context. Taking into account dependencies within the displacement field of the distortion by using a pseudo two-dimensional hidden Markov model with additional distortion possibilities further improves the error rate. Using the methods presented in this work, the previous best error rate of 8.0% on the used medical data could be considerably reduced by about one third to 5.3%.

Writer Adaptive Training and Writing Variant Model Refinement for Offline Arabic Handwriting Recognition

We present a writer adaptive training and writer clustering approach for an HMM based Arabic handwriting recognition system to handle different handwriting styles and their variations. Additionally, a writing variant model refinement for specific writing variants is proposed.Current approaches try to compensate the impact of different writing styles during preprocessing and normalization steps.Writer adaptive training with a CMLLR based feature adaptation is used to train writer dependent models. An unsupervised writer clustering with Bayesian information criterion based stopping condition for a CMLLR based feature adaptation during a two-pass decoding process is used to cluster different handwriting styles of unknown test writers.The proposed methods are evaluated on the IFN/ENIT Arabic handwriting database.

Confidence scores for acoustic model adaptation

This paper focuses on confidence scores for use in acoustic model adaptation. Frame-based confidence estimates are used in linear transform (CMLLR and MLLR) and MAP adaptation. We show that adaptation approaches with a limited number of free parameters such as linear transform-based approaches are robust in the face of frame labeling errors whereas adaptation approaches with a large number of free parameters such as MAP are sensitive to the quality of the supervision and hence benefit most from use of confidences. Different approaches for using confidence information in adaptation are investigated. This analysis shows that a thresholding approach is effective in that it improves the frame labeling accuracy with little detrimental effect on frame recall. Experimental results show an absolute WER reduction of 2.1% over a CMLLR adapted system on a video transcription task.

Audio segmentation for speech recognition using segment features

Audio segmentation is an essential preprocessing step in several audio processing applications with a significant impact e.g. on speech recognition performance. We introduce a novel framework which combines the advantages of different well known segmentation methods. An automatically estimated log-linear segment model is used to determine the segmentation of an audio stream in a holistic way by a maximum a posteriori decoding strategy, instead of classifying change points locally. A comparison to other segmentation techniques in terms of speech recognition performance is presented, showing a promising segmentation quality of our approach.

Advances in Arabic broadcast news transcription at RWTH

This paper describes the RWTH speech recognition system for Arabic. Several design aspects of the system, including cross-adaptation, multiple system design and combination, are analyzed. We summarize the semi-automatic lexicon generation for Arabic using a statistical approach to grapheme-to-phoneme conversion and pronunciation statistics. Furthermore, a novel ASR-based audio segmentation algorithm is presented. Finally, we discuss practical approaches for parallelized acoustic training and memory efficient lattice rescoring. Systematic results are reported on recent GALE evaluation corpora.