Exploiting speaker segmentations for automatic role detection. An application to broadcast news documents

Bigot, Benjamin; Pinquier, Julien

doi:10.1109/cbmi.2010.5529900

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…It outperforms the results in [6], which also used TIMIT synthesized data, based on algorithms in [5]. These results are still very competitive, compared to other algorithms using real world conversations [7], [13].…”

Section: Discussionmentioning

confidence: 79%

Speaker change detection using features through a neural network speaker classifier

Iyer

Cheluvaraja

et al. 2017

2017 Intelligent Systems Conference (IntelliSys)

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The mechanism proposed here is for real-time speaker change detection in conversations, which firstly trains a neural network text-independent speaker classifier using indomain speaker data. Through the network, features of conversational speech from out-of-domain speakers are then converted into likelihood vectors, i.e. similarity scores comparing to the in-domain speakers. These transformed features demonstrate very distinctive patterns, which facilitates differentiating speakers and enable speaker change detection with some straight-forward distance metrics. The speaker classifier and the speaker change detector are trained/tested using speech of the first 200 (indomain) and the remaining 126 (out-of-domain) male speakers in TIMIT respectively. For the speaker classification, 100% accuracy at a 200 speaker size is achieved on any testing file, given the speech duration is at least 0.97 seconds. For the speaker change detection using speaker classification outputs, performance based on 0.5, 1, and 2 seconds of inspection intervals were evaluated in terms of error rate and F1 score, using synthesized data by concatenating speech from various speakers. It captures close to 97% of the changes by comparing the current second of speech with the previous second, which is very competitive among literature using other methods.

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Section: Discussionmentioning

confidence: 79%

Speaker change detection using features through a neural network speaker classifier

Iyer

Cheluvaraja

et al. 2017

2017 Intelligent Systems Conference (IntelliSys)

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“…AVPD can be used in many different kinds of applications by both professionals and the general public. One of the most interesting applications of people diarization to video documents is the detection of major casts and their roles, for example, the anchor persons in TV news or principal characters in movies [6,10,11,14,18,19]. Their occurrences provide good indices for organizing and presenting video content.…”

Section: Introductionmentioning

confidence: 99%

Audiovisual diarization of people in video content

Khoury

Sènac

Joly

2012

Multimed Tools Appl

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Audio-Visual People Diarization (AVPD) is an original framework that simultaneously improves audio, video, and audiovisual diarization results. Following a literature review of people diarization for both audio and video content and their limitations, which includes our own contributions, we describe a proposed method for associating both audio and video information by using co-occurrence matrices and present experiments which were conducted on a corpus containing TV news, TV debates, and movies. Results show the effectiveness of the overall diarization system and confirm the gains audio information can bring to video indexing and vice versa.

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“…The most commonly used are the Gaussian mixture models and the hidden Markov models. 10,11,14,26,37,40 Also widely used are the support vector machines, 11,14,38,39,41 the artificial neural networks, 10 the k-nearest neighbor algorithm, 14,38 the decision trees, 10,38 the genetic algorithms, 2 the fuzzy logic 42 and boosting techniques. 41,43 Related architectures incorporate fusion frameworks among recognition models 28,44 and combination of model-based and distance based algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9] Concerning the audio data, the automatic analysis of the audio signals can offer the users useful information. In the case of broadcast news, automatic processing is related to tasks such as sound recognition, 10,11 speaker recognition, 12 anchor detection, 13 role detection, [14][15][16] story boundary detection, 2,17,18 summary construction from anchor talking, 9,19 channel's quality detection, 20 sound event detection, 21,22 non-linguistic humanproduced sounds detection, 5,6,[23][24][25] audio type segmentation in sport games, 4,26,27 highlight scene extraction from sports games, 3 violence scene detection, 28 music characteristics classification, 29,30 jingle detection, 1 commercial block detection, 8 voice activity detection, 31 language recognition, 32 emotion recognition 33 and speech recognition. 34 Sound recognition is the cornerstone of analysis as typically precedes the other stages.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Audio Feature Space Clustering for Automatic Sound Recognition in Radio Broadcast News

Theodorou

Mporas

Lazaridis

et al. 2017

Int. J. Artif. Intell. Tools

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Aiming to an automatic sound recognizer for radio broadcasting events, a methodology of clustering the audio feature space using the discrimination ability of the audio descriptors as a criterion, is investigated in this work. From a given and close set of audio events, commonly found in broadcast news transmissions, a large set of audio descriptors is extracted and their data-driven ranking of relevance is clustered, providing a more robust feature selection. The clusters of the feature space are feeding machine learning algorithms implemented as classification models during the experimental evaluation. This methodology showed that support vector machines provide significantly good results, considering the achieved accuracy due to their ability of coping well in high dimensionality experimental conditions.

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Exploiting speaker segmentations for automatic role detection. An application to broadcast news documents

Cited by 7 publications

References 12 publications

Speaker change detection using features through a neural network speaker classifier

Speaker change detection using features through a neural network speaker classifier

Audiovisual diarization of people in video content

Data-Driven Audio Feature Space Clustering for Automatic Sound Recognition in Radio Broadcast News

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