Integration of evolutionary computation algorithms and new AUTO-TLBO technique in the speaker clustering stage for speaker diarization of broadcast news
| Autor: | Salah Hajji, Karim Dabbabi, Adnen Cherif |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Acoustics and Ultrasonics
Computer science lcsh:QC221-246 02 engineering and technology lcsh:QA75.5-76.95 Evolutionary computation 030507 speech-language pathology & audiology 03 medical and health sciences Speaker diarization Genetic algorithm 0202 electrical engineering electronic engineering information engineering EA algorithms TLBO technique Electrical and Electronic Engineering DE algorithm Cluster analysis business.industry Particle swarm optimization PSO algorithm Pattern recognition Speaker diarisation Task (computing) GA algorithm Differential evolution lcsh:Acoustics. Sound Canopy clustering algorithm 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science Artificial intelligence 0305 other medical science business Algorithm |
| Zdroj: | EURASIP Journal on Audio, Speech, and Music Processing, Vol 2017, Iss 1, Pp 1-15 (2017) |
| ISSN: | 1687-4722 |
| DOI: | 10.1186/s13636-017-0117-1 |
| Popis: | The task of speaker diarization is to answer the question "who spoke when?" In this paper, we present different clustering approaches which consist of Evolutionary Computation Algorithms (ECAs) such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, and Differential Evolution (DE) algorithm as well as Teaching-Learning-Based Optimization (TLBO) technique as a new optimization technique at the aim to optimize the number of clusters in the speaker clustering stage which remains a challenging problem. Clustering validity indexes, such as Within-Class Distance (WCD) index, Davies and Bouldin (DB) index, and Contemporary Document (CD) index, is also used in order to make a correction for each possible grouping of speakers' segments. The proposed algorithms are evaluated on News Broadcast database (NDTV), and their performance comparisons are made between each another as well as with some well-known clustering algorithms. Results show the superiority of the new AUTO-TLBO technique in terms of comparative results obtained on NDTV, RT-04F, and ESTER datasets of News Broadcast. |
| Databáze: | OpenAIRE |
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