25:
164:. There are two main kinds of clustering strategies. The first one is by far the most popular and is called Bottom-Up. The algorithm starts in splitting the full audio content in a succession of clusters and progressively tries to merge the redundant clusters in order to reach a situation where each cluster corresponds to a real speaker. The second clustering strategy is called
139:
systems, by providing the speaker’s true identity. It is used to answer the question "who spoke when?" Speaker diarisation is a combination of speaker segmentation and speaker clustering. The first aims at finding speaker change points in an audio stream. The second aims at grouping together speech
268:
Sahidullah, Md; Patino, Jose; Cornell, Samuele; Yin, Ruiking; Sivasankaran, Sunit; Bredin, Herve; Korshunov, Pavel; Brutti, Alessio; Serizel, Romain; Vincent, Emmanuel; Evans, Nicholas; Marcel, Sebastien; Squartini, Stefano; Barras, Claude (2019-11-06). "The Speed
Submission to DIHARD II:
143:
With the increasing number of broadcasts, meeting recordings and voice mail collected every year, speaker diarisation has received much attention by the speech community, as is manifested by the specific evaluations devoted to it under the auspices of the
383:
Park, Tae Jin; Kanda, Naoyuki; Dimitriadis, Dimitrios; Han, Kyu J.; Watanabe, Shinji; Narayanan, Shrikanth (2021-11-26). "A Review of
Speaker Diarization: Recent Advances with Deep Learning".
168:
and starts with one single cluster for all the audio data and tries to split it iteratively until reaching a number of clusters equal to the number of speakers. A 2010 review can be found at
131:) is the process of partitioning an audio stream containing human speech into homogeneous segments according to the identity of each speaker. It can enhance the readability of an
236:(last update: December 2010; version: 0.3): SHoUT is a software package developed at the University of Twente to aid speech recognition research. SHoUT is a Dutch acronym for
201:(last repository update: July 2016; last release: February 2013, version: 3.0): ALIZE Diarization System, developed at the University Of Avignon, a release 2.0 is available
210:(last repository update: May 2014; last release: January 2010, version: 1.2): AudioSeg is a toolkit dedicated to audio segmentation and classification of audio streams.
125:
339:
219:(last repository update: August 2022, last release: July 2022, version: 2.0): pyannote.audio is an open-source toolkit written in Python for speaker diarization.
145:
148:
for telephone speech, broadcast news and meetings. A leading list tracker of speaker diarization research can be found at Quan Wang's github repo.
469:
228:(last repository update: September 2022): Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
42:
108:
89:
61:
46:
68:
488:
75:
493:
35:
175:
57:
198:
179:
459:
420:
245:
312:
160:
to model each of the speakers, and assign the corresponding frames for each speaker with the help of a
225:
425:
216:
202:
161:
136:
446:
384:
270:
169:
132:
82:
465:
438:
291:
207:
430:
233:
165:
194:
There are some open source initiatives for speaker diarisation (in alphabetical order):
249:
482:
450:
183:
157:
359:
135:
by structuring the audio stream into speaker turns and, when used together with
24:
419:(2). IEEE/ACM Transactions on Audio, Speech, and Language Processing: 356–370.
434:
229:
442:
220:
335:
211:
408:
248:(last release: September 2013, version: 8.4.1): LIUM_SpkDiarization tool
241:
156:
In speaker diarisation, one of the most popular methods is to use a
389:
275:
290:
Zhu, Xuan; Barras, Claude; Meignier, Sylvain; Gauvain, Jean-Luc.
311:
Kotti, Margarita; Moschou, Vassiliki; Kotropoulos, Constantine.
18:
16:
Partitioning a stream of human speech by identity of speaker
413:
IEEE Transactions on Audio, Speech, and
Language Processing
292:"Improved speaker diarization using speaker identification"
238:
Speech
Recognition Research at the University of Twente
174:
More recently, speaker diarisation is performed via
49:. Unsourced material may be challenged and removed.
409:"Speaker diarization: A review of recent research"
140:segments on the basis of speaker characteristics.
146:National Institute of Standards and Technology
182:computing and methodological developments in
8:
424:
388:
274:
109:Learn how and when to remove this message
190:Open source speaker diarisation software
336:"Rich Transcription Evaluation Project"
260:
269:Contributions & Lessons Learned".
313:"Speaker Segmentation and Clustering"
7:
47:adding citations to reliable sources
461:Fundamentals of Speaker Recognition
14:
152:Main types of diarisation systems
23:
34:needs additional citations for
133:automatic speech transcription
1:
360:"Awesome Speaker Diarization"
510:
435:10.1109/TASL.2011.2125954
199:ALIZE Speaker Diarization
458:Beigi, Homayoon (2011).
407:Anguera, Xavier (2012).
178:leveraging large-scale
464:. New York: Springer.
158:Gaussian mixture model
58:"Speaker diarisation"
43:improve this article
364:awesome-diarization
246:LIUM SpkDiarization
162:Hidden Markov Model
137:speaker recognition
122:Speaker diarisation
489:Speech recognition
494:Speech processing
471:978-0-387-77591-3
119:
118:
111:
93:
501:
475:
454:
428:
395:
394:
392:
380:
374:
373:
371:
370:
356:
350:
349:
347:
346:
332:
326:
325:
323:
322:
317:
308:
302:
301:
299:
298:
287:
281:
280:
278:
265:
114:
107:
103:
100:
94:
92:
51:
27:
19:
509:
508:
504:
503:
502:
500:
499:
498:
479:
478:
472:
457:
426:10.1.1.470.6149
406:
403:
398:
382:
381:
377:
368:
366:
358:
357:
353:
344:
342:
334:
333:
329:
320:
318:
315:
310:
309:
305:
296:
294:
289:
288:
284:
267:
266:
262:
258:
226:pyAudioAnalysis
192:
176:neural networks
154:
115:
104:
98:
95:
52:
50:
40:
28:
17:
12:
11:
5:
507:
505:
497:
496:
491:
481:
480:
477:
476:
470:
455:
402:
399:
397:
396:
375:
351:
327:
303:
282:
259:
257:
254:
253:
252:
243:
231:
223:
217:pyannote.audio
214:
205:
191:
188:
153:
150:
117:
116:
31:
29:
22:
15:
13:
10:
9:
6:
4:
3:
2:
506:
495:
492:
490:
487:
486:
484:
473:
467:
463:
462:
456:
452:
448:
444:
440:
436:
432:
427:
422:
418:
414:
410:
405:
404:
400:
391:
386:
379:
376:
365:
361:
355:
352:
341:
337:
331:
328:
314:
307:
304:
293:
286:
283:
277:
272:
264:
261:
255:
250:
247:
244:
242:
239:
235:
232:
230:
227:
224:
221:
218:
215:
212:
209:
206:
203:
200:
197:
196:
195:
189:
187:
185:
184:deep learning
181:
177:
172:
170:
167:
163:
159:
151:
149:
147:
141:
138:
134:
130:
127:
123:
113:
110:
102:
91:
88:
84:
81:
77:
74:
70:
67:
63:
60: –
59:
55:
54:Find sources:
48:
44:
38:
37:
32:This article
30:
26:
21:
20:
460:
416:
412:
401:Bibliography
378:
367:. Retrieved
363:
354:
343:. Retrieved
330:
319:. Retrieved
306:
295:. Retrieved
285:
263:
237:
193:
173:
155:
142:
128:
121:
120:
105:
99:January 2012
96:
86:
79:
72:
65:
53:
41:Please help
36:verification
33:
129:diarization
483:Categories
390:2101.09624
369:2024-09-17
345:2012-01-25
321:2012-01-25
297:2012-01-25
276:1911.02388
256:References
69:newspapers
451:206602044
443:1558-7916
421:CiteSeerX
208:Audioseg
166:top-down
83:scholar
468:
449:
441:
423:
85:
78:
71:
64:
56:
447:S2CID
385:arXiv
316:(PDF)
271:arXiv
234:SHoUT
90:JSTOR
76:books
466:ISBN
439:ISSN
340:NIST
62:news
431:doi
180:GPU
45:by
485::
445:.
437:.
429:.
417:20
415:.
411:.
362:.
338:.
240:.
186:.
171:.
126:or
474:.
453:.
433::
393:.
387::
372:.
348:.
324:.
300:.
279:.
273::
251:.
222:.
213:.
204:.
124:(
112:)
106:(
101:)
97:(
87:·
80:·
73:·
66:·
39:.
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.