Speaker diarization and linking of large corpora

Ferras, Marc; Boudard, Herve

doi:10.1109/slt.2012.6424236

Cited by 27 publications

(19 citation statements)

References 10 publications

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“…ILP is also integrated into the open-source toolbox for broadcast news diarization LIUM [32]. In the case of the diarization of a big collection of recordings, the clusters which are defined after applying diarization separately in each recording can be used to perform a two-stage clustering approach, and compress the information [37].…”

Section: Speaker Diarizationmentioning

confidence: 99%

Web Radio Automation for Audio Stream Management in the Era of Big Data

2020

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Radio is evolving in a changing digital media ecosystem. Audio-on-demand has shaped the landscape of big unstructured audio data available online. In this paper, a framework for knowledge extraction is introduced, to improve discoverability and enrichment of the provided content. A web application for live radio production and streaming is developed. The application offers typical live mixing and broadcasting functionality, while performing real-time annotation as a background process by logging user operation events. For the needs of a typical radio station, a supervised speaker classification model is trained for the recognition of 24 known speakers. The model is based on a convolutional neural network (CNN) architecture. Since not all speakers are known in radio shows, a CNN-based speaker diarization method is also proposed. The trained model is used for the extraction of fixed-size identity d-vectors. Several clustering algorithms are evaluated, having the d-vectors as input. The supervised speaker recognition model for 24 speakers scores an accuracy of 88.34%, while unsupervised speaker diarization scores a maximum accuracy of 87.22%, as tested on an audio file with speech segments from three unknown speakers. The results are considered encouraging regarding the applicability of the proposed methodology.

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Section: Speaker Diarizationmentioning

confidence: 99%

Web Radio Automation for Audio Stream Management in the Era of Big Data

2020

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“…For the annotation of speaker identities in the raw broadcast data, one viable approach is to link the speakers [37,38] appearing in the reference data with known identities to the (pseudo) speaker labels provided by a speaker diarization system. Speaker linking is achieved by clustering all speakers appearing in the reference and raw broadcast data based on the speaker similarity scores assigned by a speaker recognition (SR) system [39].…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised acoustic model training for speech with code-switching

Yılmaz

McLaren

Heuvel

et al. 2018

Speech Communication

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In the FAME! project, we aim to develop an automatic speech recognition (ASR) system for Frisian-Dutch code-switching (CS) speech extracted from the archives of a local broadcaster with the ultimate goal of building a spoken document retrieval system. Unlike Dutch, Frisian is a low-resourced language with a very limited amount of manually annotated speech data. In this paper, we describe several automatic annotation approaches to enable using of a large amount of raw bilingual broadcast data for acoustic model training in a semi-supervised setting. Previously, it has been shown that the best-performing ASR system is obtained by two-stage multilingual deep neural network (DNN) training using 11 hours of manually annotated CS speech (reference) data together with speech data from other high-resourced languages. We compare the quality of transcriptions provided by this bilingual ASR system with several other approaches that use a language recognition system for assigning language labels to raw speech segments at the front-end and using monolingual ASR resources for transcription. We further investigate automatic annotation of the speakers appearing in the raw broadcast data by first labeling with (pseudo) speaker tags using a speaker diarization system and then linking to the known speakers appearing in the reference data using a speaker recognition system. These speaker labels are essential for speaker-adaptive training in the proposed setting. We train acoustic models using the manually and automatically annotated data and run recognition experiments on the development and test data of the FAME! speech corpus to quantify the quality of the automatic annotations. The ASR and CS detection results demonstrate the potential of using automatic language and speaker tagging in semi-supervised bilingual acoustic model training.

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“…Application domains of cross-show diarization include radio and TV [1,2,3,4,5], phone [6,7,8,9] or meeting recordings [10]. The state-of-the-art systems based on i-vector/PLDA framework require speaker annotated datasets including speaker segments and identities.…”

Section: Introductionmentioning

confidence: 99%

Iterative PLDA Adaptation for Speaker Diarization

Lan¹,

Charlet²,

Larcher³

et al. 2016

Interspeech 2016

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This paper investigates iterative PLDA adaptation for crossshow speaker diarization applied to small collections of French TV archives based on an i-vector framework. Using the target collection itself for unsupervised adaptation, PLDA parameters are iteratively tuned while score normalization is applied for convergence. Performances are compared, using combinations of target and external data for training and adaptation. The experiments on two distinct target corpora show that the proposed framework can gradually improve an existing system trained on external annotated data. Such results indicate that performing speaker diarization on small collections of unlabeled audio archives should only rely on the availability of a sufficient bootstrap system, which can be incrementally adapted to every target collection. The proposed framework also widens the range of acceptable speaker clustering thresholds for a given performance objective.

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Speaker diarization and linking of large corpora

Cited by 27 publications

References 10 publications

Web Radio Automation for Audio Stream Management in the Era of Big Data

Web Radio Automation for Audio Stream Management in the Era of Big Data

Semi-supervised acoustic model training for speech with code-switching

Iterative PLDA Adaptation for Speaker Diarization

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