MediaParl: Bilingual mixed language accented speech database

Imseng, David; Bourlard, Hervé; Caesar, Holger; Garner, Philip N.; Lecorvé, Gwénolé; Nanchen, Alexandre

doi:10.1109/slt.2012.6424233

Cited by 22 publications

(38 citation statements)

References 6 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even if this study does not present novel training or decoding techniques for DNNs, the rigorous evaluation of two powerful implementations of DNN architectures on this particular database yield conclusions that can easily be generalized. The hypotheses under investigation are closely aligned with the development of a production ASR system (in this case, the MediaParl application [5]). We therefore believe that the hypotheses and experimental results are useful for other researchers and for building modern ASR systems using foreign data.…”

Section: Discussionmentioning

confidence: 98%

“…Recently in the context of the MediaParl project, a bilingual spoken language database was introduced [5] to help to recognize and annotate the proceedings of the cantonal parliament of Valais in Switzerland. The main characteristics of the parliamentary speech are that it is reverberant and in two languages -French ("standard" French) and German (accented "standard" German).…”

Section: Overviewmentioning

confidence: 99%

“…The main objective of this paper is to investigate whether foreign data can improve the performance of an ASR system already trained using matched (in-domain and in-language) data. We chose a recent bilingual speech database [5] for this work. The data was released in the context of the MediaParl project, which aims to recognize and annotate the proceedings of the cantonal parliament of Valais in Switzerland.…”

Section: Introductionmentioning

confidence: 99%

“…The main characteristics of the parliamentary speech are that it is in two languages (French and German), accented, and reverberant. Initial results were presented in [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploiting foreign resources for DNN-based ASR

Motlíček

Potard

Garner

et al. 2015

J AUDIO SPEECH MUSIC PROC.

Self Cite

View full text Add to dashboard Cite

Manual transcription of audio databases for the development of automatic speech recognition (ASR) systems is a costly and time-consuming process. In the context of deriving acoustic models adapted to a specific application, or in low-resource scenarios, it is therefore essential to explore alternatives capable of improving speech recognition results. In this paper, we investigate the relevance of foreign data characteristics, in particular domain and language, when using this data as an auxiliary data source for training ASR acoustic models based on deep neural networks (DNNs). The acoustic models are evaluated on a challenging bilingual database within the scope of the MediaParl project. Experimental results suggest that in-language (but out-of-domain) data is more beneficial than in-domain (but out-of-language) data when employed in either supervised or semi-supervised training of DNNs. The best performing ASR system, an HMM/GMM acoustic model that exploits DNN as a discriminatively trained feature extractor outperforms the best performing HMM/DNN hybrid by about 5 % relative (in terms of WER). An accumulated relative gain with respect to the MFCC-HMM/GMM baseline is about 30 % WER.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The main characteristics of the parliamentary speech are that it is in two languages (French and German), accented, and reverberant. Initial results were presented in [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting foreign resources for DNN-based ASR

Motlíček

Potard

Garner

et al. 2015

J AUDIO SPEECH MUSIC PROC.

Self Cite

View full text Add to dashboard Cite

show abstract

“…We study the performance of an ASR system built on 20 hours of French MediaParl data [4]. MediaParl contains German and French data recorded at the bilingual Valais parliament (Valais is a bilingual Swiss canton).…”

Section: Introductionmentioning

confidence: 99%

Exploiting un-transcribed foreign data for speech recognition in well-resourced languages

Potard

Motlíček

Nanchen

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

Manual transcription of audio databases for automatic speech recognition (ASR) training is a costly and time-consuming process. State-of-the-art hybrid ASR systems that are based on deep neural networks (DNN) can exploit un-transcribed foreign data during unsupervised DNN pre-training or semisupervised DNN training. We investigate the relevance of foreign data characteristics, in particular domain and language.Using three different datasets of the MediaParl and Ester databases, our experiments suggest that domain and language are equally important. Foreign data recorded under matched conditions (language and domain) yields the most improvement. The resulting ASR system yields about 5% relative improvement compared to the baseline system only trained on transcribed data. Our studies also reveal that the amount of foreign data used for semi-supervised training can be significantly reduced without degrading the ASR performance if confidence measure based data selection is employed.

show abstract

Finnish parliament ASR corpus

Virkkunen

Rouhe

Phan

et al. 2023

Lang Resources & Evaluation

View full text Add to dashboard Cite

Public sources like parliament meeting recordings and transcripts provide ever-growing material for the training and evaluation of automatic speech recognition (ASR) systems. In this paper, we publish and analyse the Finnish Parliament ASR Corpus, the most extensive publicly available collection of manually transcribed speech data for Finnish with over 3000 h of speech and 449 speakers for which it provides rich demographic metadata. This corpus builds on earlier initial work, and as a result the corpus has a natural split into two training subsets from two periods of time. Similarly, there are two official, corrected test sets covering different times, setting an ASR task with longitudinal distribution-shift characteristics. An official development set is also provided. We developed a complete Kaldi-based data preparation pipeline and ASR recipes for hidden Markov models (HMM), hybrid deep neural networks (HMM-DNN), and attention-based encoder-decoders (AED). For HMM-DNN systems, we provide results with time-delay neural networks (TDNN) as well as state-of-the-art wav2vec 2.0 pretrained acoustic models. We set benchmarks on the official test sets and multiple other recently used test sets. Both temporal corpus subsets are already large, and we observe that beyond their scale, HMM-TDNN ASR performance on the official test sets has reached a plateau. In contrast, other domains and larger wav2vec 2.0 models benefit from added data. The HMM-DNN and AED approaches are compared in a carefully matched equal data setting, with the HMM-DNN system consistently performing better. Finally, the variation of the ASR accuracy is compared between the speaker categories available in the parliament metadata to detect potential biases based on factors such as gender, age, and education.

show abstract

MediaParl: Bilingual mixed language accented speech database

Cited by 22 publications

References 6 publications

Exploiting foreign resources for DNN-based ASR

Exploiting foreign resources for DNN-based ASR

Exploiting un-transcribed foreign data for speech recognition in well-resourced languages

Finnish parliament ASR corpus

Contact Info

Product

Resources

About