The czech broadcast conversation corpus

This paper presents the final version of the Czech Broadcast Conversation Corpus that will shortly be released at the Linguistic Data Consortium (LDC). The corpus contains 72 recordings of a radio discussion program, which yields about 33 hours of transcribed conversational speech from 128 speakers. The release does not only include verbatim transcripts and speaker information, but also structural metadata (MDE) annotation that involves labeling of sentence-like unit boundaries, marking of non-content words like filled pauses and discourse markers, and annotation of speech disfluencies. The MDE annotation is based on the LDC's annotation standard for English, with changes applied to accommodate phenomena that are specific for Czech. In addition to its importance to speech recognition, speaker diarization, and structural metadata extraction research, the corpus is also useful for linguistic analysis of conversational Czech

The Czech Broadcast Conversation Corpus

This paper presents the final version of the Czech Broadcast Conversation Corpus that will shortly be released at the Linguistic Data Consortium (LDC). The corpus contains 72 recordings of a radio discussion program, which yields about 33 hours of transcribed conversational speech from 128 speakers. The release does not only include verbatim transcripts and speaker information, but also structural metadata (MDE) annotation that involves labeling of sentence-like unit boundaries, marking of non-content words like filled pauses and discourse markers, and annotation of speech disfluencies. The MDE annotation is based on the LDC's annotation standard for English, with changes applied to accommodate phenomena that are specific for Czech. In addition to its importance to speech recognition, speaker diarization, and structural metadata extraction research, the corpus is also useful for linguistic analysis of conversational Czech

International Comparable Corpus : Challenges in building multilingual spoken and written comparable corpora

This paper reports on the efforts of twelve national teams in building the International Comparable Corpus (ICC; https://korpus.cz/icc) that will contain highly comparable datasets of spoken, written and electronic registers. The languages currently covered are Czech, Finnish, French, German, Irish, Italian, Norwegian, Polish, Slovak, Swedish and, more recently, Chinese, as well as English, which is considered to be the pivot language. The goal of the project is to provide much-needed data for contrastive corpus-based linguistics. The ICC corpus is committed to the idea of re-using existing multilingual resources as much as possible and the design is modelled, with various adjustments, on the International Corpus of English (ICE). As such, ICC will contain approximately the same balance of forty percent of written language and 60 percent of spoken language distributed across 27 different text types and contexts. A number of issues encountered by the project teams are discussed, ranging from copyright and data sustainability to technical advances in data distribution.Peer reviewe

How to Find Common Ground, or Overlaps in Turn-Taking in Two TV Programs

The aim of this paper is to present some remarks concerning the phenomenon of overlaps in spoken language. Simultaneous speech has been studied since the 1970s, when Sacks, Schegloff and Jefferson described their theory of turn-taking organisation in conversation. In the literature, overlaps are sometimes considered as going against the principle of minimizing gaps and interruptions, something that speakers should avoid. We would like to show that overlaps do not appear randomly but that speakers can use them functionally and that they can even fulfil cooperative functions. Our study is based on data from Dialog, a special multimedia corpus of spoken Czech, which collects public dialogues — recordings and transcripts of discussion programs broadcast on Czech TV stations.244

Multitask Detection of Speaker Changes, Overlapping Speech and Voice Activity Using wav2vec 2.0

Self-supervised learning approaches have lately achieved great success on a broad spectrum of machine learning problems. In the field of speech processing, one of the most successful recent self-supervised models is wav2vec 2.0. In this paper, we explore the effectiveness of this model on three basic speech classification tasks: speaker change detection, overlapped speech detection, and voice activity detection. First, we concentrate on only one task -- speaker change detection -- where our proposed system surpasses the previously reported results on four different corpora, and achieves comparable performance even when trained on out-of-domain data from an artificially designed dataset. Then we expand our approach to tackle all three tasks in a single multitask system with state-of-the-art performance on the AMI corpus. The implementation of the algorithms in this paper is publicly available at https://github.com/mkunes/w2v2_audioFrameClassification.Comment: To appear at ICASSP 202

Improving coreference resolution by using conversational metadata

In this paper, we propose the use of metadata contained in documents to improve coreference resolution. Specifically, we quantify the impact of speaker and turn information on the performance of our coreference system, and show that the metadata can be effectively encoded as features of a statistical resolution system, which leads to a statistically significant improvement in performance.