In this paper, we describe the RWTH speech recognition system for English lectures developed within the Translectures project. A difficulty in the development of an English lectures recognition system, is the high rat...
详细信息
ISBN:
(纸本)9781479928941
In this paper, we describe the RWTH speech recognition system for English lectures developed within the Translectures project. A difficulty in the development of an English lectures recognition system, is the high ratio of non-native speakers. We address this problem by using very effective deep bottleneck features trained on multilingual data. The acoustic model is trained on large amounts of data from different domains and with different dialects. Large improvements are obtained from unsupervised acoustic adaptation. Another challenge is the frequent use of technical terms and the wide range of topics. In our recognition system, slides, which are attached to most lectures, are used for improving lexical coverage and language model adaptation.
In this paper we investigate different n-gram language models that are defined over an open lexicon. We introduce a character-level language model and combine it with a standard word-level language model in a back off...
详细信息
In this paper we investigate different n-gram language models that are defined over an open lexicon. We introduce a character-level language model and combine it with a standard word-level language model in a back off fashion. The character-level language model is redefined and renormalized to assign zero probability to words from a fixed vocabulary. Furthermore we present a way to interpolate language models created at the word and character levels. The computation of character-level probabilities incorporates the across-word context. We compare perplexities on all words from the test set and on in-lexicon and OOV words separately on corpora of English and Arabic text.
We present an iterative technique to generate phrase tables for SMT, which is based on force-aligning the training data with a modified translation decoder. Different from previous work, we completely avoid the use of...
详细信息
Automatically clustering words from a monolingual or bilingual training corpus into classes is a widely used technique in statistical natural language processing. We present a very simple and easy to implement method ...
详细信息
In this paper, we propose a novel reordering model based on sequence labeling techniques. Our model converts the reordering problem into a sequence labeling problem, i.e. a tagging task. Results on five Chinese-Englis...
详细信息
In this paper we address the problem of solving substitution ciphers using a beam search approach. We present a conceptually consistent and easy to implement method that improves the current state of the art for decip...
详细信息
In this paper we show that even for the case of 1:1 substitution ciphers-which encipher plaintext symbols by exchanging them with a unique substitute-finding the optimal decipherment with respect to a bigram language ...
详细信息
We present a novel approach for translation model (TM) adaptation using phrase training. The proposed adaptation procedure is initialized with a standard general-domain TM, which is then used to perform phrase trainin...
详细信息
We present a novel approach for translation model (TM) adaptation using phrase training. The proposed adaptation procedure is initialized with a standard general-domain TM, which is then used to perform phrase trainin...
详细信息
In this paper, we investigate the combination of hidden Markov models and convolutional neural networks for handwritten word recognition. The convolutional neural networks have been successfully applied to various com...
详细信息
ISBN:
(纸本)9781479903566
In this paper, we investigate the combination of hidden Markov models and convolutional neural networks for handwritten word recognition. The convolutional neural networks have been successfully applied to various computer vision tasks, including handwritten character recognition. In this work, we show that they can replace Gaussian mixtures to compute emission probabilities in hidden Markov models (hybrid combination), or serve as feature extractor for a standard Gaussian HMM system (tandem combination). The proposed systems outperform a basic HMM based on either decorrelated pixels or handcrafted features. We validated the approach on two publicly available databases, and we report up to 60% (Rimes) and 35% (IAM) relative improvement compared to a Gaussian HMM based on pixel values. The final systems give comparable results to recurrent neural networks, which are the best systems since 2009.
暂无评论