Frank Wessel

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In this paper, we present several confidence measures for large vocabulary continuous speech recognition. We propose to estimate the confidence of a hypothesized word directly as its posterior probability, given all acoustic observations of the utterance. These probabilities are computed on word graphs using a forward–backward algorithm. We also study the(More)
For large vocabulary continuous speech recognition systems, the amount of acoustic training data is of crucial importance. In the past, large amounts of speech were thus recorded from various sources and had to be transcribed manually. It is thus desirable to train a recognizer with as little manually transcribed acoustic data as possible. Since(More)
This paper studies the overall effect of language modeling on perplexity and word error rate, starting from a trigram model with a standard smoothing method up to complex state–of–the– art language models: (1) We compare different smoothing methods, namely linear vs. absolute discounting, interpolation vs. backing-off, and back-off functions based on(More)
In this paper, the interdependence of language models and discriminative training for large vocabulary speech recognition is investigated. In addition, a constrained recognition approach using word graphs is presented for the efficient determination of alternative word sequences for discriminative training. Experiments have been carried out on the ARPA Wall(More)
Automatic recognition of conversational speech tends to have higher word error rates (WER) than read speech. Improvements gained from unsupervised speaker adaptation methods like Maximum Likelihood Linear Regression (MLLR) [1] are reduced because of their sensitivity to recognition errors in the first pass. We show that a more detailed modeling of(More)
The aim of this paper is to describe the experiences gained in the field of language modelling during the LE-3 ARISE (Automatic Railway Information Systems for Europe) project. All of the different techniques presented in this paper are related to the field of Spoken Dialogue Systems, and they cope with the issues of limited amount of training material and(More)
In this paper we present and compare several confidence measures for large vocabulary continuous speech recognition. We show that posterior word probabilities computed on word graphs and N-best lists clearly outperform non-probabilistic confidence measures, e.g. the acoustic stability and the hypothesis density. In addition, we prove that the estimation of(More)
In this paper, we introduce a new concept, the time frame error rate. We show that this error rate is closely correlated with the word error rate and use it to overcome the mismatch between Bayes’ decision rule which aims at minimizing the expected sentence error rate and the word error rate which is used to assess the performance of speech recognition(More)