The Spoken Wikipedia Corpus collection: Harvesting, alignment and an application to hyperlistening

Baumann, Timo; Köhn, Arne; Hennig, Felix

doi:10.1007/s10579-017-9410-y

Cited by 30 publications

(16 citation statements)

References 10 publications

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“…The data has been prepared as a corpus [12]. 3 Importantly, the annotation includes a linguistic sentence segmentation and tokenization and the relation of original and normalized text has been preserved, allowing the timings of the aligned normalized text to be mapped to each original text token, thus bridging the gap between speech and language processing.…”

Section: Data and Setupmentioning

confidence: 99%

“…We limit our analysis to the German sub-corpus of the Spoken Wikipedia which contains some 1000 articles totaling 386 h of audio (360 h after VAD) and 3 M word tokens read by 350 different speakers [12]. The alignment favors quality over coverage and hence only about 70 % of the word tokens have alignment information available.…”

Section: Data and Setupmentioning

confidence: 99%

“…We analyze, in a large and diverse corpus of read German encyclopaedic material as collected from the Spoken Wikipedia 1 [12], the relation of syntactic dependency structure and prosodic realization using linear mixed effects models [13]. We analyze the correlation of prosodic features measured on a word and the syntactic function assigned to that word (or the word's head).…”

Section: Introductionmentioning

confidence: 99%

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An Empirical Analysis of the Correlation of Syntax and Prosody

Köhn

Baumann

Dörfler³

2018

Interspeech 2018

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The relation of syntax and prosody (the syntax-prosody interface) has been an active area of research, mostly in linguistics and typically studied under controlled conditions. More recently, prosody has also been successfully used in the data-based training of syntax parsers. However, there is a gap between the controlled and detailed study of the individual effects between syntax and prosody and the large-scale application of prosody in syntactic parsing with only a shallow analysis of the respective influences. In this paper, we close the gap by investigating the significance of correlations of prosodic realization with specific syntactic functions using linear mixed effects models in a very large corpus of read-out German encyclopedic texts. Using this corpus, we are able to analyze prosodic structuring performed by a diverse set of speakers while they try to optimize factual content delivery. After normalization by speaker, we obtain significant effects, e. g. confirming that the subject function, as compared to the object function, has a positive effect on pitch and duration of a word, but a negative effect on loudness.

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Section: Data and Setupmentioning

confidence: 99%

Section: Data and Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Empirical Analysis of the Correlation of Syntax and Prosody

Köhn

Baumann

Dörfler³

2018

Interspeech 2018

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“…• Text-Speech Aligner: we use the text-speech aligner published by [18] which uses a variation of the SailAlign algorithm [19] implemented using Sphinx-4 [20]. The alignments are stored in a format that guarantees the original text to remain unchanged (which is important to be able to combine them with syntactic and other annotations).…”

Section: Processing Toolsmentioning

confidence: 99%

Automatic Detection of Enjambment in German Readout Poetry

Hussein¹,

Meyer-Sickendiek²,

Baumann³

2018

Speech Prosody 2018

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One of the most important patterns in ancient as well as modern poetry is the enjambment, the continuation of a sentence beyond the end of a line, couplet, or stanza. The paper reports first activities towards the development of a digital tool to analyze the accentuation of poetic enjambments in readout poetry. The aim in this contribution is to recognize two forms of enjambment (emphasized and unemphasized) in poems using audio and text data. We use data from lyrikline which is a major online portal for spoken poetry whereas poems are read aloud by the original authors. We identified by hermeneutical means based on literary analysis a total of 69 poems being characteristic for the use of enjambments in modern and postmodern German poetry and train classifiers to differentiate the emphasized/unemphasized categorization. A remarkable result of our automated analyses (and to our knowledge the first data-driven analysis of this kind) is the identification of a cultural difference in the accentuation of enjambments: statistically speaking, poets from the former GDR tend to emphasize the enjambment, whereas poets from the FRG do not. We use features derived from speech-to-text alignment and statistical parsing information such as pause lengths, number of lines with verbs, and number of lines with punctuation. The best classification results, calculated by the F-measure, for the both types of enjambment (emphasized/unemphasized) is 0.69.

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“…Various strategies have been proposed to collect speech and text resources for technology development, for example harvesting existing data like broadcast news and online publications, crowd-sourcing, web crawling, dedicated data collection campaigns, etcetera [7][8][9][10][11][12][13]. Both data types are required for language and speech technology development, and constructing comprehensive text corpora is just as important as creating speech resources.…”

Section: Introductionmentioning

confidence: 99%

The Usefulness of Imperfect Speech Data for ASR Development in Low-Resource Languages

Badenhorst¹,

Wet

2019

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When the National Centre for Human Language Technology (NCHLT) Speech corpus was released, it created various opportunities for speech technology development in the 11 official, but critically under-resourced, languages of South Africa. Since then, the substantial improvements in acoustic modeling that deep architectures achieved for well-resourced languages ushered in a new data requirement: their development requires hundreds of hours of speech. A suitable strategy for the enlargement of speech resources for the South African languages is therefore required. The first possibility was to look for data that has already been collected but has not been included in an existing corpus. Additional data was collected during the NCHLT project that was not included in the official corpus: it only contains a curated, but limited subset of the data. In this paper, we first analyze the additional resources that could be harvested from the auxiliary NCHLT data. We also measure the effect of this data on acoustic modeling. The analysis incorporates recent factorized time-delay neural networks (TDNN-F). These models significantly reduce phone error rates for all languages. In addition, data augmentation and cross-corpus validation experiments for a number of the datasets illustrate the utility of the auxiliary NCHLT data.

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The Spoken Wikipedia Corpus collection: Harvesting, alignment and an application to hyperlistening

Cited by 30 publications

References 10 publications

An Empirical Analysis of the Correlation of Syntax and Prosody

An Empirical Analysis of the Correlation of Syntax and Prosody

Automatic Detection of Enjambment in German Readout Poetry

The Usefulness of Imperfect Speech Data for ASR Development in Low-Resource Languages

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