1,046 research outputs found

    Speech recognition experiments with audiobooks

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    Under real-life conditions several factors may be present that make the automatic recognition of speech difficult. The most obvious examples are background noise, peculiarities of the speaker's voice, sloppy articulation and strong emotional load. These all pose difficult problems for robust speech recognition, but it is not exactly clear how much each contributes to the difficulty of the task. In this paper we examine the abilities of our best recognition technologies under near-ideal conditions. The optimal conditions will be simulated by working with the sound material of an audiobook, in which most of the disturbing factors mentioned above are absent. Firstly pure phone recognition experiments will be performed, where neural net-based technologies will also be tried as well as the conventional Hidden Markov Models. Then we move on to large vocabulary recognition, where morphbased language models are applied to improve the performance of the standard word-based technology. The tests clearly justify our assertion that audiobooks pose a much easier recognition task than real-life databases. In both types of tasks we report the lowest error rates we have achieved so far in Hungarian continuous speech recognition

    Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation

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    Recent works in spoken language translation (SLT) have attempted to build end-to-end speech-to-text translation without using source language transcription during learning or decoding. However, while large quantities of parallel texts (such as Europarl, OpenSubtitles) are available for training machine translation systems, there are no large (100h) and open source parallel corpora that include speech in a source language aligned to text in a target language. This paper tries to fill this gap by augmenting an existing (monolingual) corpus: LibriSpeech. This corpus, used for automatic speech recognition, is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. After gathering French e-books corresponding to the English audio-books from LibriSpeech, we align speech segments at the sentence level with their respective translations and obtain 236h of usable parallel data. This paper presents the details of the processing as well as a manual evaluation conducted on a small subset of the corpus. This evaluation shows that the automatic alignments scores are reasonably correlated with the human judgments of the bilingual alignment quality. We believe that this corpus (which is made available online) is useful for replicable experiments in direct speech translation or more general spoken language translation experiments.Comment: LREC 2018, Japa

    MLS: A Large-Scale Multilingual Dataset for Speech Research

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    This paper introduces Multilingual LibriSpeech (MLS) dataset, a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages, including about 44.5K hours of English and a total of about 6K hours for other languages. Additionally, we provide Language Models (LM) and baseline Automatic Speech Recognition (ASR) models and for all the languages in our dataset. We believe such a large transcribed dataset will open new avenues in ASR and Text-To-Speech (TTS) research. The dataset will be made freely available for anyone at http://www.openslr.org
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