The RWTH English lecture recognition system

Wiesler, Simon; Irie, Kazuki; Tüske, Zoltán; Schlüter, Ralf; Ney, Hermann

doi:10.1109/icassp.2014.6854208

Cited by 3 publications

(2 citation statements)

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“…They are trigram LMs of vocabulary size 64k words. The selection of these LMs has been carried out on the basis of literature, as these models are widely used for large vocabulary speech recognition [30][31][32][33][34]. A description of LMs is given here.…”

Section: Comparison To Existing Lmsmentioning

confidence: 99%

Ameliorated language modelling for lecture speech recognition of Indian English

Phull

Kumar

2018

Sādhanā

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A great amount of research is growing towards the automatic transcription of lectures that consist of numerous information and knowledge that could be helpful to the educational systems and institutes. In large vocabulary speech recognition, language model plays a paramount role in reducing the humongous search space. However, language modelling is very brittle when moving from one domain to another or when moving from read speech to spontaneous speech. Also, lecture speech recognition will have some of the characteristics of spontaneous speech. Hence, it is very challenging to build the language model for this task. In this paper, a judicious approach to adapt the language model in a way where the language model will be in close proximity to the topic spoken in the lecture speech has been depicted. The evaluation of the language model is devised using the proposed approach with the existing language models such as CMU Sphinx, Gigaword and HUB-4. We observed the results analysis that the language models devised from the proposed approach outperform from the existing language models in terms of word error rate, perplexity and out of vocabulary rate. Analysis shows that the presented two-phase approach has resulted in an average decrease of the word error rate to be approximately 14% and the perplexity is decreased by half on average.

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Section: Comparison To Existing Lmsmentioning

confidence: 99%

Ameliorated language modelling for lecture speech recognition of Indian English

Phull

Kumar

2018

Sādhanā

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“…Nowadays, automatic transcriptions of spontaneous speech in moderately noisy environments have reached an accurate enough quality ( [1,2,3]). This quality can be even better when ASR systems are adapted to specific scenarios ( [4,5,6,7,8,9]). Nonetheless, ASR is still far from producing error-free transcriptions and, consequently, its performance in many applications is not completely satisfactory.…”

Section: Introductionmentioning

confidence: 99%

Speaker-adapted confidence measures for speech recognition of video lectures

Sanchez-Cortina

Andrés-Ferrer

Sanchís

et al. 2016

Computer Speech & Language

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Automatic Speech Recognition applications can benefit from a confidence measure (CM) to predict the reliability of the output. Previous works showed that a word-dependent naïve Bayes (NB) classifier outperforms the conventional word posterior probability as a CM. However, a discriminative formulation usually renders improved performance due to the available training techniques. Taking this into account, we propose a logistic regression (LR) classifier defined with simple input functions to approximate to the NB behaviour. Additionally, as a main contribution, we propose to adapt the CM to the speaker in cases in which it is possible to identify the speakers, such as online lecture repositories. The experiments have shown that speaker-adapted models outperform their non-adapted counterparts on two difficult tasks from English (videoLectures.net) and Spanish (poliMedia) educational lectures. They have also shown that the NB model is clearly superseded by the proposed LR classifier.

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