EML Submission to Albayzin 2018 Speaker Diarization Challenge

Ghahabi, Omid; Fischer, Volker

doi:10.21437/iberspeech.2018-44

Cited by 2 publications

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“…4 show the same experiments as in Table 2 on the evaluation set, yielding the same conclusions. We participated in the challenge with only LDA transformed supervectors and the same low cost online diarization algorithm [27] and obtained very competitive or even better results compared to the other more expensive offline diarization techniques.…”

Section: Resultsmentioning

confidence: 99%

Speaker-Corrupted Embeddings for Online Speaker Diarization

Ghahabi

Fischer

2019

Interspeech 2019

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Speaker diarization is more challenging in presence of background noise or music, frequent speaker changes, and cross talks. In an online scenario, the decision should be made at time, given only the current short segment and the speakers detected in the past, which makes the task even harder. In this work, an online robust speaker diarization algorithm is proposed in which speech segments are represented by low dimensional vectors referred to as speaker-corrupted embeddings. The proposed speaker embedding network is a deep neural network which takes speaker-corrupted supervectors as input, uses variable ReLU (VReLU) as an activation function, and tries to discriminate the background speakers. Speaker corruption is performed by adding supervectors built by 20 speech frames from other speakers to the supervectors of a given speaker. It is shown that speaker corruption, VReLU, and input dropout increase the generalization power of the proposed network. To increase the robustness, the proposed embeddings are concatenated with LDA transformed supervectors. Experimental results on the Albayzin 2018 evaluation set show a competitive accuracy, more robustness, and much lower computational cost compared to typical offline algorithms.

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Section: Resultsmentioning

confidence: 99%

Speaker-Corrupted Embeddings for Online Speaker Diarization

Ghahabi

Fischer

2019

Interspeech 2019

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“…The first contrastive system used ICMCfeatures (infinite impulse response-constant Q, Mel-frequency cepstral coefficients), one second uniform segmentation, binary key (BK) representation, and AHC, while the second one used MFCC features, bidirectional LSTM based speaker change detection, triplet-loss neural embedding representation, and affinity propagation clustering. • G19-EML [35]. European Media Laboratory GmbH, Germany.…”

Section: Closed-set Condition Systemsmentioning

confidence: 99%

Albayzin 2018 Evaluation: The IberSpeech-RTVE Challenge on Speech Technologies for Spanish Broadcast Media

et al. 2019

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The IberSpeech-RTVE Challenge presented at IberSpeech 2018 is a new Albayzin evaluation series supported by the Spanish Thematic Network on Speech Technologies (Red Temática en Tecnologías del Habla (RTTH)). That series was focused on speech-to-text transcription, speaker diarization, and multimodal diarization of television programs. For this purpose, the Corporacion Radio Television Española (RTVE), the main public service broadcaster in Spain, and the RTVE Chair at the University of Zaragoza made more than 500 h of broadcast content and subtitles available for scientists. The dataset included about 20 programs of different kinds and topics produced and broadcast by RTVE between 2015 and 2018. The programs presented different challenges from the point of view of speech technologies such as: the diversity of Spanish accents, overlapping speech, spontaneous speech, acoustic variability, background noise, or specific vocabulary. This paper describes the database and the evaluation process and summarizes the results obtained. in Spanish [8][9][10][11], and more recently, the Multi-Genre Broadcast (MGB) Challenge with data in English and Arabic 2 [12][13][14]. In other areas apart from broadcast speech, several evaluation campaigns have been proposed such as the ones organized in the scope of the Zero Resource Speech Challenge [15,16], the TC-STAR evaluation on recordings of the European Parliament's sessions in English and Spanish [5], or the MediaEval evaluation of multimodal search and hyperlinking [17].As a way to measure the performance of different techniques and approaches, in this 2018 edition, the IberSpeech-RTVE Challenge Evaluation campaign was proposed in three different conditions: speech-to-text transcription (STT), speaker diarization (SD), and multimodal diarization (MD). Twenty-two teams registered to the challenge, and eighteen submitted systems in at least one of the three proposed tasks. In this paper, we describe the challenge and the data provided by the organization to the participants. We also provide a description of the systems presented to the evaluation, their results, and a set of conclusions that can be drawn from this evaluation campaign.This paper is organized as follows. In Section 2, the RTVE2018 database is presented. Section 3 describes the three evaluation tasks, speech-to-text transcription, speaker diarization, and multimodal diarization. Section 4 provides a brief description of the main features of the submitted systems. Section 5 presents results, and Section 6 gives conclusions.

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EML Submission to Albayzin 2018 Speaker Diarization Challenge

Cited by 2 publications

References 2 publications

Speaker-Corrupted Embeddings for Online Speaker Diarization

Speaker-Corrupted Embeddings for Online Speaker Diarization

Albayzin 2018 Evaluation: The IberSpeech-RTVE Challenge on Speech Technologies for Spanish Broadcast Media

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