Europarl-ASR: A Large Corpus of Parliamentary Debates for Streaming ASR Benchmarking and Speech Data Filtering/Verbatimization

Abstract

We introduce Europarl-ASR, a large speech and text corpus of parliamentary debates including 1 300 hours of transcribed speeches and 70 million tokens of text in English extracted from European Parliament sessions. The training set is labelled with the Parliament's non-fully-verbatim official transcripts, timealigned. As verbatimness is critical for acoustic model training, we also provide automatically noise-filtered and automatically verbatimized transcripts of all speeches based on speech data filtering and verbatimization techniques. Additionally, 18 hours of transcribed speeches were manually verbatimized to build reliable speaker-dependent and speaker-independent development/test sets for streaming ASR benchmarking. The availability of manual non-verbatim and verbatim transcripts for dev/test speeches makes this corpus useful for the assessment of automatic filtering and verbatimization techniques. This paper describes the corpus and its creation, and provides off-line and streaming ASR baselines for both the speaker-dependent and speaker-independent tasks using the three training transcription sets. The corpus is publicly released under an open licence.

A Bilingual Basque–Spanish Dataset of Parliamentary Sessions for the Development and Evaluation of Speech Technology

“…are suitably covered due to their commercial interest, while languages spoken by few people or lacking the support of governments struggle to be even considered by major technological giants. This issue is not new and has been addressed in two different ways: (1) by fostering the production of language (spoken and text) resources, many of them from parliamentary speeches [2][3][4][5][6][7][8]; and (2) by leveraging the resources produced for other languages, e.g., by adjusting (finetuning) models or systems trained on multilingual data [9,10]. In the case of Basque, to compensate for the lack of interest of private companies, efforts have focused on producing data.…”

Streaming Automatic Speech Recognition with Hybrid Architectures and Deep Neural Network Models

“…with well-defined and widely used latency metrics. From our group we proposed tasks following this idea, but there is still more work to do (Iranzo-Sánchez et al 2020;Díaz-Munío et al 2021). This kind of datasets will frame the research and boost the interest from academia to work on real-world streaming conditions, and not only on academic tasks.…”