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-automat...
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ISBN:
(纸本)9781424413690;1424413699
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.
This paper describes the development of the RWTH Mandarin LVCSR system. Different acoustic front-ends together with multiple system cross-adaptation are used in a two stage decoding framework. We describe the system i...
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ISBN:
(纸本)9781424413690;1424413699
This paper describes the development of the RWTH Mandarin LVCSR system. Different acoustic front-ends together with multiple system cross-adaptation are used in a two stage decoding framework. We describe the system in detail and present systematic recognition results. Especially, we compare a variety of approaches for cross-adapting to multiple systems. During the development we did a comparative study on different methods for integrating tone and phoneme posterior features. Furthermore, we apply lattice based consensus decoding and system combination methods. In these methods, the effect of minimizing character instead of word errors is compared. The final system obtains a character error rate of 17.7% on the GALE 2006 evaluation data.
We present the RWTH phrase-based statistical machine translation system designed for the translation of Arabic speech into English text. This system was used in the Global Autonomous language Exploitation (GALE) Go/No...
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ISBN:
(纸本)9781424413690;1424413699
We present the RWTH phrase-based statistical machine translation system designed for the translation of Arabic speech into English text. This system was used in the Global Autonomous language Exploitation (GALE) Go/No-Go Translation Evaluation 2007. Using a two-pass approach, we first generate n-best translation candidates and then rerank these candidates using additional models. We give a short review of the decoder as well as of the models used in both passes. We stress the difficulties of spoken language translation, i.e. how to combine the recognition and translation systems and how to compensate for missing punctuation. In addition, we cover our work on domain adaptation for the applied language models. We present translation results for the official GALE 2006 evaluation set and the GALE 2007 development set.
We give an overview of the RWTH phrase-based statistical machine translation system that was used in the evaluation campaign of the International Workshop on Spoken language Translation (IWSLT) 2006. The system was ra...
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Word posterior probabilities are a common approach for confidence estimation in automatic speech recognition and machine translation. We will generalize this idea and introduce n-gram posterior probabilities and show ...
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We present discriminative reordering models for phrase-based statistical machine translation. The models are trained using the maximum entropy principle. We use several types of features: based on words, based on word...
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In this paper, we consider the use of multiple acoustic features of the speech signal for robust speech recognition. We investigate the combination of various auditory based (Mel Frequency Cepstrum Coefficients, Perce...
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We give an overview of the RWTH phrase-based statistical machine translation system that was used in the evaluation campaign of the International Workshop on Spoken language Translation 2005. We use a two pass approac...
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In this paper, we consider the use of multiple acoustic features of the speech signal for continuous speech recognition. A novel articulatory motivated acoustic feature is introduced, namely the spectrum derivative fe...
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In this paper, we consider the use of multiple acoustic features of the speech signal for continuous speech recognition. A novel articulatory motivated acoustic feature is introduced, namely the spectrum derivative feature. The new feature is tested in combination with the standard Mel Frequency Cepstral Coefficients (MFCC) and the voicedness features. Linear Discriminant Analysis is applied to find the optimal combination of different acoustic features. Experiments have been performed on small and large vocabulary tasks. Significant improvements in word error rate have been obtained by combining the MFCC feature with the articulatory motivated voicedness and spectrum derivative features: improvements of up to 25% on the small-vocabulary task and improvements of up to 4% on the large-vocabulary task relative to using MFCC alone with the same overall number of parameters in the system.
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