Daniel Mllett, Christoph Neukirchen, Joe Rottl, Gerhard Rigoll
Decision tree-based state clustering has emerged in recent years as the most popular approach for clustering the states of context dependent hidden Markov model based speech recognizers. The...
Controlling the Complexity of HMM Systems by (2008)
Christoph Neukirchen, Gerhard Rigoll
This paper introduces a method for regularization of HMM systems that avoids parameter overfitting caused by insufficient training data. Regularization is done by augmenting the EM training method by...
J. R. Ohm, Steffen Hohmann, ...
Scattered radiation in cone-beam computed tomography: analysis, quantification and compensation Von der Fakultät für Elektrotechnik und Informationstechnik
Christoph Neukirchen, Gerhard Rigoll
Introduction While Neural Networks (NN) have been very successfully applied to static pattern recognition tasks like image classification, pure NN-speech recognition approaches often perform quite...
Time Series Classification using Hidden Markov Models and Neural Networks (2007)
Christoph Neukirchen, Gerhard Rigoll
This paper gives a brief overview on statistical classification of time series using Hidden Markov Models. To overcome some limitations and assumptions made in the Hidden Markov Model framework two...
Controlling the Complexity of HMM Systems by Regularization (2007)
Christoph Neukirchen, Gerhard Rigoll
This paper introduces a method for regularization of HMM systems that avoids parameter overfitting caused by insufficient training data. Regularization is done by augmenting the EM training method by...
Optimal combination of Neural Networks and discrete statistical pattern classifiers (2007)
Christoph Neukirchen, Gerhard Rigoll
. This paper deals with the problem of combination of Neural Networks (NN) and traditional statistical pattern classifiers. It is shown that a Neural Network can be used to replace the vector...
REDUCED LEXICON TREES FOR DECODING IN A MMI-CONNECTIONIST/HMM SPEECH RECOGNITION SYSTEM (2007)
Christoph Neukirchen, Daniel Willett, Gerhard Rigoll
The presented work deals with the experimental identification of parts in a tree based decoder lexicon, that are more important for decoding efficiency compared to less important lexicon parts. Three...
A continuous density interpretation of discrete HMM systems and MMI-neural networks (2001)
Christoph Neukirchen, Jörg Rottl, Daniel Willett, Gerhard Rigoll, Senior Member
Abstract—The subject of this paper is the integration of the traditional vector quantizer (VQ) and discrete hidden Markov models (HMM) combination in the mixture emission density framework commonly...
Ducoder - The Duisburg University Lvcsr Stackdecoder (2000)
Daniel Willett, Christoph Neukirchen, Gerhard Rigoll
With this paper, we present the DUcoder, the LVCSR decoder developed at Duisburg University. The decoder performs the Viterbi search for the most probable word sequence in recognition systems that...
Thesis (doctoral) - Universität, Duisburg, 1998.
Stefan Müller, Stefan M Uller, Stefan Eickeler, Christoph Neukirchen, Bernd Winterstein
In this paper, a new approach to identification of handwritten symbols in arbitrary complex environments is presented. 20 different pictograms drawn in different backgrounds can be identified with a...
Experiments In Topic Indexing Of Broadcast News Using Neural Networks (1999)
Christoph Neukirchen, Daniel Willett, Gerhard Rigoll
The paper deals with the problem of extracting topic information from news show stories by statistical methods. It is shown that the traditional topic-dependent n-gram language modeling approach can...
Jörg Rottland, J Org Rottl, Christoph Neukirchen, Daniel Willett, Gerhard Rigoll
This paper describes, how to perform speaker adaptation for a hybrid large vocabulary speech recognition system. The hybrid system is based on a Maximum Mutual Information Neural Network (MMINN),...
Daniel Willett, Christoph Neukirchen, Jörg Rottland, J Org Rottl, Gerhard Rigoll
Decision tree-based state clustering has emerged in recent years as the most popular approach for clustering the states of context dependent hidden Markov model based speech recognizers. The...
Confidence measures for hmm-based speech recognition (1998)
Daniel Willett, Andreas Worm, Christoph Neukirchen, Gerhard Rigoll
In this paper, we describe our work on the field of confidence measures for HMM-based speech recognition. Confidence measures are a means of estimating the recognition reliability for single words of...
Christoph Neukirchen, Daniel Willett, Stefan Eickeler, Stefan Müller
In previous work about hybrid speech recognizers with discrete HMMs we have shown that VQs, that are trained according to an MMI criterion, are well suited for ML estimated Bayes classifiers. This is...
Soft State-Tying For HMM-Based Speech Recognition (1998)
Christoph Neukirchen, Daniel Willett, Gerhard Rigoll
This paper introduces a method for regularization of HMM systems that avoids parameter overfitting causedby insufficient training data. Regularization is done by augmenting the EM training method by...
Efficient Search With Posterior Probability Estimates In Hmm-Based Speech Recognition (1998)
Daniel Willett, Christoph Neukirchen, Gerhard Rigoll
In this paper we present the methods we developed to estimate posterior probabilities for HMM states in continuous and discrete HMM-based speech recognition systems and several ways to speed up...
Christoph Neukirchen, Gerhard Rigoll
This paper deals with the construction and optimization of a hybrid speech recognition system that consists of a combination of a neural vector quantizer (VQ) and discrete HMMs. In our investigations...
Daniel Willett, Christoph Neukirchen, Jörg Rottland
The estimation of the HMM parameters has always been a major issue in the design of speech recognition systems. Discriminative objectives like Maximum Mutual Information (MMI) or Minimum...
A New Hybrid System Based On MMI-Neural Networks For The RM Speech Recognition Task (1996)
Gerhard Rigoll, Christoph Neukirchen, Jörg Rottland
We present a hybrid speech recognition system for speaker independent continuous speech recognition. The system combines a novel information theory based neural network (NN) paradigm and discrete...