The most widely used acoustic feature extraction methods of current automatic speech recognition (ASR) systems are based on the assumption of stationarity. In this paper we extensively evaluate a recently introduced f...
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Automatic sign languagerecognition (ASLR) is a special case of automatic speech recognition (ASR) and computer vision (CV) and is currently evolving from using artificial labgenerated data to using 'real-life'...
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In this paper, three different voicing features are studied as additional acoustic features for continuous speech recognition. The harmonic product spectrum based feature is extracted in frequency domain while the aut...
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In this paper, three different voicing features are studied as additional acoustic features for continuous speech recognition. The harmonic product spectrum based feature is extracted in frequency domain while the autocorrelation and the average magnitude difference based methods work in time domain. The algorithms produce a measure of voicing for each time frame. The voicing measure was combined with the standard Mel Frequency Cepstral Coefficients (MFCC) using linear discriminant analysis to choose the most relevant features. Experiments have been performed on small and large vocabulary tasks. The three different voicing measures combined with MFCCs resulted in similar improvements in word error rate: improvements of up to 14% on the small-vocabulary task and improvements of up to 6% on the large-vocabulary task relative to using MFCC alone with the same overall number of parameters in the system.
This paper describes the statistical machine translation system developed at RWTH Aachen University for the English→Romanian translation task of the ACL 2016 First Conference on Machine Translation (WMT 2016). We com...
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Embedding and projection matrices are commonly used in neural language models (NLM) as well as in other sequence processing networks that operate on large vocabularies. We examine such matrices in fine-tuned language ...
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In this paper, we enhance the traditional confusion network system combination approach with an additional model trained by a neural network. This work is motivated by the fact that the commonly used binary system vot...
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This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the translation task of the NAACL 2012 Seventh Workshop on Statistical Machine Translation (WMT 2012). We ...
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This work investigates the alignment problem in state-of-the-art multi-head attention models based on the transformer architecture. We demonstrate that alignment extraction in transformer models can be improved by aug...
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Checkpoint averaging is a simple and effectivemethod to boost the performance of convergedneural machine translation models. The calculation is cheap to perform and the fact thatthe translation improvement almost come...
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This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the translation task of the ACL 2013 Eighth Workshop on Statistical Machine Translation (WMT 2013). We par...
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