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The Lincoln stress-resistant HMM CSR has been extended to large vocabulary continuous speech for both speaker-dependent (SD) and speaker-independent (SI) tasks. Performance on the DARPA Resource Manage- ment task (991 …...

This paper reports on two contributions to large vocabu- lary continuous speech recognition. First, we present a new paradigm for speaker-independent (SI) training of hid- den Markov models (HMM), which …...

We report on some recent improvements to an HMM- based, continuous speech recognition system which is being developed at AT&T Bell Laboratories. These advances, which include the incorporation of inter-word, …...

How to capture important acoustic clues and estimate essen- tial parameters reliably is one of the central issues in speech recognition, since we will never have sufficient training data to …...

Language models for speech recognition con- centrate solely on recognizing the words that were spoken. In this paper, we advocate re- defining the speech recognition problem so that its goal …...

We study the impact of richer syntac- tic dependencies on the performance of the structured language model (SLM) along three dimensions: parsing accu- racy (LP/LR), perplexity (PPL) and word- error-rate …...

A new almost-parsing language model incorporat- ing multiple knowledge sources that is based upon the concept of Constraint Dependency Grammars is presented in this paper. Lexical features and syn- tactic …...

In this paper, we introduce a generative prob- abilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text …...

With the increasing use of research paper search engines, such as CiteSeer, for both lit- erature search and hiring decisions, the accu- racy of such systems is of paramount impor- …...

The Colorado Literacy Tutor (CLT) is a technology-based literacy program, designed on the basis of cognitive theory and scientifically motivated reading research, which aims to improve literacy and student achievement …...

This paper describes discriminative language modeling for a large vocabulary speech recognition task. We con- trast two parameter estimation methods: the perceptron algorithm, and a method based on conditional random …...

We present the first application of the head-driven statistical parsing model of Collins (1999) as a si- multaneous language model and parser for large- vocabulary speech recognition. The model is …...

To improve the robustness in multimodal input interpretation, this paper presents a new salience driven approach. This approach is based on the observation that, during multimodal conversation, information from deictic …...

. In this paper, we present a language model based on clusters obtained by applying regular expressions to the training data and, thus, discriminating several different sentence types as, e.g. …...

During the last years there has been grow- ing interest in using neural networks for language modeling. In contrast to the well known back-off n-gram language models, the neural network …...

AbstractThis work adresses the use of confidence measures for extracting well recognized words with very low error rate from automatically transcribed segments in a unsupervised way. We present and compare …...

This paper presents a corrective model for speech recognition of inflected lan- guages. The model, based on a discrim- inative framework, incorporates word n- grams features as well as factored …...

In this paper, we explore statistical lan- guage modelling for a speech-enabled MP3 player application by generating a corpus from the interpretation grammar written for the application with the Gram- …...

Mixture modelling is a standard pattern classification technique. However, in statistical machine translation, the use of mixture modelling is still un- explored. Two main advantages of the mixture approach are …...

Adapting language models across styles and topics, such as for lecture transcrip- tion, involves combining generic style models with topic-specific content rele- vant to the target document. In this work, …...

AbstractMost African countries follow an oral tradition system to transmit their cultural, scientific and historic heritage through generations. This ancestral knowledge accumulated during centuries is today threatened of disappearing. This …...

We address the task of unsupervised topic segmentation of speech data operating over raw acoustic information. In contrast to ex- isting algorithms for topic segmentation of speech, our approach does …...

Speech recognition in many morphologi- cally rich languages suffers from a very high out-of-vocabulary (OOV) ratio. Earlier work has shown that vocabulary decomposition methods can practically solve this problem for …...

Evaluation and error analysis of machine translation output are important but difficult tasks. In this work, we propose a novel method for obtaining more details about ac- tual translation errors …...

In domains with insufficient matched training data, language models are often constructed by interpolating component models trained from partially matched corpora. Since the n- grams from such corpora may not …...

Electronic written texts used in computer- mediated interactions (e-mails, blogs, chats, etc) present major deviations from the norm of the language. This paper presents an comparative study of systems aiming …...

For Arabic, diacritizing written text is impor- tant for many NLP tasks. In the work presented here, we investigate the quality of a diacritization approach, with a high success rate …...

In this work, we try a hybrid methodology for language modeling where both morphological decomposition and factored language model- ing (FLM) are exploited to deal with the com- plex morphology …...

AbstractWe present an evaluation framework to enable developers of information seeking, transaction based spoken dialogue systems to compare the robustness of natural language understanding (NLU) approaches across varying levels of …...

We introduce a novel mechanism for incorporating articulatory dynamics into speech recognition with the theory of task dynamics. This system reranks sentence- level hypotheses by the likelihoods of their hypothetical …...

Hierarchical discourse segmentation is a use- ful technology, but it is difficult to eval- uate. I propose an error measure based on the word error rate of Beeferman et al. …...

AbstractThis paper investigates the effectiveness of online temporal language model adaptation when applied to a Thai broadcast news transcription task. Our adaptation scheme works as follow: first an initial language …...

A re-scoring strategy is proposed that makes it feasible to capture more long-distance de- pendencies in the natural language. Two pass strategies have become popular in a num- ber of …...

We describe the development of a pro- totype of an open source rule-based Icelandic→English MT system, based on the Apertium MT framework and IceNLP, a natural language processing toolkit for …...

In this paper, we adopt an n-best rescoring scheme using pitch-accent patterns to improve automatic speech recognition (ASR) perfor- mance. The pitch-accent model is decoupled from the main ASR system, …...

Translation results suffer when a standard phrase-based statistical machine transla- tion system is used for translating long sen- tences. The translation output will not pre- serve the same word order …...

Among various neural network language models (NNLMs), recurrent neural network-based lan- guage models (RNNLMs) are very competitive in many cases. Most current RNNLMs only use one single feature stream, i.e., …...

AbstractThis article describes the development of a bidirectional shallow-transfer based machine translation system for Spanish and Aragonese, based on the Apertium platform, reusing the resources provided by other translators built …...

We present a novel scheme of language modeling for a spoken dialogue system by effectively filtering query sentences collected via a Web site of wisdom of crowds. Our goal is …...

This paper describes a phrase-based ma- chine translation approach to normalize Dutch user-generated content (UGC). We compiled a corpus of three different so- cial media genres (text messages, message board …...

AbstractIn this paper we describe an effort to create a corpus and phonetic dictionary for Tunisian Arabic Automatic Speech Recognition (ASR). The corpus, named TARIC (Tunisian Arabic Railway Interaction Corpus) …...

We propose a language modeling (LM) approach incorporating interpolated distanced n-grams in a Dirichlet class language model (DCLM) (Chien and Chueh, 2011) for speech recognition. The DCLM relaxes the bag-of-words …...

Some revisions of documents can change the meaning of passages, while others merely re-phrase or improve style. In a multi-author workflow, assisting readers to assess whether a revision changes mean- …...

AbstractThis paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predicts the dialogue act and …...

AbstractAutomatic Speech recognition (ASR) is one of the most widely used components in spoken language processing applications. ASR errors are of varying importance with respect to the application, making error …...

AbstractThe multi-level adaptive networks (MLAN) technique is a cross-lingual adaptation framework where a bottleneck (BN) layer in a deep neural network (DNN) trained in a source language is used for …...

AbstractIn an increasingly globalized world, there is a rising demand for speech recognition systems. Systems for languages like English, German or French do achieve a decent performance, but there exists …...

One of the big challenges connected to large vocabulary Arabic speech recognition is the limit of vocabulary, which causes high out- of-vocabulary words. Also, the Arabic language characteristics are another …...

Motivated by a project to create a system for people who are deaf or hard-of-hearing that would use automatic speech recognition (ASR) to produce real-time text captions of spoken English …...

We develop a post-processing system to efficiently correct errors from noisy optical character recognition (OCR) in a 2.7 million word Faroese corpus. 7.6% of the words in the original OCR …...

Developing language resources is an important task when creating a speech recognition system for a less-resourced language. In this paper we describe available language resources and their preparation for use …...

State-of-the-art speech recognition systems rely heavily on three basic components: an acoustic model, a pronunciation lexicon and a language model. To build these components, a researcher needs linguistic as well …...

AbstractWe present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings. Unlike conventional speech recognition models, our model learns longer conversational-context information …...

The paper proposes a system that compensates most of the noise in a text in natural language caused by technical imperfection of the input device such as keyboard or scanner …...

AbstractWe introduce the first attempt at automatic speech recognition (ASR) in Inuktitut, as a representative for polysynthetic, low-resource languages, like many of the 900 Indigenous languages spoken in the Americas. …...

AbstractSpeech recognition has seen dramatic improvements in the last decade, though those improvements have focused primarily on adult speech. In this paper, we assess child-directed speech recognition and leverage a …...

AbstractWe present an open-source speech corpus for the Kazakh language. The Kazakh speech corpus (KSC) contains around 332 hours of transcribed audio comprising over 153,000 utterances spoken by participants from …...

Automatic speech recognition (ASR) in San- skrit is interesting, owing to the various lin- guistic peculiarities present in the language. The Sanskrit language is lexically productive, undergoes euphonic assimilation of …...

AbstractVoice-driven communication aids are one of the methods commonly used by patients with dysarthria. However, this type of assistive devices demands a large amount of voice data from patients to …...

AbstractOptical character recognition (OCR) is crucial for a deeper access to historical collections. OCR needs to account for orthographic variations, typefaces, or language evolution (i.e., new letters, word spellings), as …...

AbstractRecent years have seen an increasing need for gender-neutral and inclusive language. Within the field of NLP, there are various mono- and bilingual use cases where gender inclusive language is …...

AbstractWe introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain. This multi-dialect speech dataset contains 2,000 hours of speech sampled at 16kHz crawled from Aljazeera news …...

AbstractThe usage of automatic speech recognition (ASR) systems are becoming omnipresent ranging from personal assistant to chatbots, home, and industrial automation systems, etc. Modern robots are also equipped with ASR …...

In the turbulent world of 2022, where mass population movements due to war and disas- ter are becoming increasingly common, lan- guage skills are more relevant than ever. Peo- ple …...

As voice user interfaces and conversational agents grow in importance, automatic speech recognition (ASR) encounters increasingly free-form and informal input data. Conversa- tional speech is at once the most challenging …...

This paper summarizes the current state of de- velopment in improving an open source subti- tling tool. This includes improvements to the speech recognition model for German, the re- placement …...

AbstractDespite recent advancements in automated speech recognition (ASR) technologies, reports of unequal performance across speakers of different demographic groups abound. At the same time, the focus on performance metrics such …...

AbstractAnglicisms are a challenge in German speech recognition. Due to their irregular pronunciation compared to native German words, automatically generated pronunciation dictionaries often contain incorrect phoneme sequences for Anglicisms. In …...

AbstractAutomatic Speech Recognition (ASR) is an efficient and widely used input method that transcribes speech signals into text. As the errors introduced by ASR systems will impair the performance of …...