project=DR, year=2024, date=, title.cs=Od modulárních k celostním systémům
diarizace řečníka, title.en=From Modular to End-to-End Speaker Diarization,
title.length=11cm, author.name=Federico Nicolás, author.surname=Landini,
author.title.p=Lic., department=UPGM, supervisor.name=Lukáš,
supervisor.surname=Burget, supervisor.title.p=doc. Ing.,
supervisor.title.a=Ph.D., cosupervisor.name=Mireia, cosupervisor.surname=Diez
Sánchez, cosupervisor.title.p=, cosupervisor.title.a=M.Sc., Ph.D.,
keywords.cs=Diarizace mluvčího, VBx, neuronová diarizace end-to-end,
simulované rozhovory, DiaPer., keywords.en=Speaker diarization, VBx, end-to-
end neural diarization, simulated conversations, DiaPer., abstract.cs=
Diarizace mluvčího se obvykle popisuje jako úloha, která určuje, „kdo kdy
mluvil“ v nahrávce. Ještě před několika lety byly všechny konkurenceschopné
přístupy modulární, tj. detekce hlasové aktivity, segmentace, extrakce
embeddingů, shlukování a detekce a zpracování překrývající se řeči byly řešeny
různými subsystémy a aplikovány jeden po druhém. Takto konstruované systémy
dosahovaly ve většině scénářů nejlepších výsledků, ale měly velké potíže
vypořádat se s překrývající se řečí. V poslední době vzbudil velkou pozornost
nástup end-to-end modelů, které jsou schopny řešit všechny aspekty diarizace
mluvčího pomocí jediného modelu a mají lepší úspěšnost, pokud jde o
překrývající se řeč. Tato práce vznikla v období koexistence těchto dvou
trendů. Popisujeme systém založený na bayesovském skrytém Markovově modelu
používaném ke shlukování x-vektorů (embeddingů mluvčích získaných pomocí
neuronové sítě), známý jako VBx, který prokázal pozoruhodnou úspěšnost na
různých souborech dat a evaluačních v různých kampaních. Popíšeme jeho výhody
a omezení a vyhodnotíme výsledky na různých relevantních korpusech. Poté
přejdeme k metodám end-to-end neuronové diarizace (EEND). Vzhledem k potřebě
velkých trénovacích sad pro trénování těchto modelů a nedostatku ručně
anotovaných diarizačních dat v dostatečném množství spočívá kompromisní řešení
v umělém generování trénovacích dat. Popíšeme přístup ke generování
syntetických dat, která se z hlediska změn a překryvů mluvčích podobají
skutečným rozhovorům. Ukážeme, jak tato metoda generování ,,simulovaných
konverzací” umožňuje dosáhnout lepší úspěšnosti než dříve navržená metoda
vytváření ,,simulovaných směsí” při trénování populárního EEND s enkodér-
dekodér atraktory (EEND-EDA). Navrhneme také nový model založený na EEND,
který nazýváme DiaPer, a ukážeme, že může dosáhnout lepších výsledků než EEND-
EDA, zejména při práci s mnoha mluvčími a při zpracování překrývající se řeči.
Nakonec porovnáváme oba systémy, VBx i DiaPer, na široké škále datových sad a
prodiskutejeme výhody každého z nich. , abstract.en= Speaker diarization is
usually referred to as the task that determines “who spoke when” in a
recording. Until a few years ago, all competitive approaches were modular,
i.e. voice activity detection, segmentation, embedding extraction, clustering
and overlapped speech detection and handling were tackled by different sub-
systems and applied in a cascaded fashion. Systems based on this framework
reached state-of-the-art performance in most scenarios but had major
difficulties dealing with overlapped speech. More recently, the advent of end-
to-end models, capable of dealing with all aspects of speaker diarization with
a single model and better performing regarding overlapped speech, has brought
high levels of attention. This thesis is framed during a period of co-
existence of these two trends. We describe a system based on a Bayesian hidden
Markov model used to cluster x-vectors (speaker embeddings obtained with a
neural network), known as VBx, which has shown remarkable performance on
different datasets and challenges. We comment on its advantages and
limitations and evaluate results on different relevant corpora. Then, we move
towards end-to-end neural diarization (EEND) methods. Due to the need for
large training sets for training these models and the lack of manually
annotated diarization data in sufficient quantities, the compromise solution
consists in generating training data artificially. We describe an approach for
generating synthetic data which resembles real conversations in terms of
speaker turns and overlaps. We show how this method generating “simulated
conversations” allows for better performance than using a previously proposed
method for creating “simulated mixtures” when training the popular EEND with
encoder-decoder attractors (EEND-EDA). We also propose a new EEND-based model,
which we call DiaPer, and show that it can perform better than EEND-EDA,
especially when dealing with many speakers and handling overlapped speech.
Finally, we compare both VBx-based and DiaPer systems on a wide variety of
corpora and comment on the advantages of each technique. , declaration=I
hereby declare that this doctoral thesis was prepared as an original work by
the author under the supervision of Dr. Lukáš Burget and Dr. Mireia Diez
Sánchez. I have listed all the literary sources, publications and other
sources, which were used during the preparation of this thesis.,
acknowledgment=Throughout my Ph.D. studies, I had the chance to interact with
many people and I would like to thank them all as everyone probably shaped
this work in some way or another. However, some deserve special mention. First
of all, my utmost gratitude goes to my advisors, Lukáš and Mireia, Mireia and
Lukáš; of whom, I believe, I am their first common “academic child”. Each of
you has played an immense role in the work I have done during the past years.
Mireia, always available to discuss ideas and results on a day-to-day basis
and provide feedback without delays. Lukáš, always happy to talk about
research ideas, even if that meant being late for his next appointment. A huge
thank you goes to you two; you certainly know how much of this work would not
have happened if it were not for your guidance. Thank you for allowing me to
work quite freely and keeping a cheerful spirit despite my bad mood when
things did not work as expected. Next in line, Honza Černocký, who has also
served as supervisor by shielding much of the administrative burden. I also
thank him for giving me the opportunity to join BUT-Speech@FIT and for running
the group as a non-bossy boss. This thesis would definitely not have happened
without his support. My appreciation extends to all the staff at BUT who made
it possible for me to work here during this time. It has been a great
experience to be in the BUT-Speech@FIT group, discussing work- and non-work-
related topics. Newer members who met me towards the end of my Ph.D. might not
know it, but I was certainly less bitter before. My advice to my past self
would be to worry less and enjoy the process more. In any case, my gratitude
goes to Speech@FIT as a whole: past/current permanent/temporary members.
Special mentions go to the Speaker ID crew, with whom I interacted more at the
beginning of my studies, and to the Diarization subgroup, who had to put up
with me during the last time. Most of you have certainly shaped, if not co-
authored, part of this work. Special among them is Pavel Matějka who showed me
how pleasant running in the forests around Brno can be. Thank you for igniting
what has become my passion for ultra-running. The few internships I was able
to do during these years were also very enriching and I thank everyone with
whom I had the chance to work. Even if the work from the internships is not
directly reflected in the thesis, the stays have certainly given me the chance
to learn many things. I would also like to thank my former advisor, Luciana
Ferrer, with whom I took my first steps in the field of speech technologies.
She made the contact with the folks in Brno that allowed me to join them for
my Ph.D. so this work would not have happened without her help. Then, I want
to thank my parents, Adriana and Fabián, who encouraged the scientific side in
me when I was little and who always supported me to keep learning. My
gratitude extends to the rest of my family who I only saw a couple of times
during these years but who were present all along the way. The same goes for
my friends who, in spite of the distance, were always with me, even if I was
not with them. Last but not least, the biggest thanks are to Katka, my wife,
for all her support through these years specially the last one. Thank you for
putting up with me, be it when I am in a bad mood or with whatever upcoming
ultra race I decide to train for., extendedabstract= Diarizace mluvčího se
obvykle označuje jako úloha, která určuje “kdo kdy mluvil” v nahrávce. Ještě
před několika lety byly všechny konkurenční přístupy modulární, tj. detekce
hlasové aktivity, segmentace, extrakce embeddingu, shlukování a detekce a
zpracování překrývající se řeči byly řešeny různými subsystémy a aplikovány
kaskádovitě. Tyto systémy dosahovaly ve většině scénářů nejlepších výsledků,
ale měly velké potíže s překrývající se řečí. V nedávné době vzbudil velkou
pozornost nástup end-to-end modelů, které jsou schopny řešit všechny aspekty
diarizace mluvčího pomocí jediného modelu a mají lepší výkonnost, pokud jde o
překrývající se řeč. Tato práce je zarámována do období koexistence těchto
dvou trendů. První část této práce popisuje VBx, systém založený na
bayesovském skrytém Markovově modelu (HMM), který se používá ke shlukování
x-vektorů (embeddingů mluvčích získaných pomocí neuronové sítě). Tento přístup
vychází z vysoké kvality x-vektorů jako reprezentací mluvčích a
pravděpodobnostního modelu lineární diskriminační analýzy (PLDA) natrénovaného
k porovnávání takových embeddingů, přičemž oba jsou natrénovány na velkém
množství dat od tisíců mluvčích. Stavy v HMM, reprezentující každého z
mluvčích v konverzaci, mají specifické modely mluvčích odvozené z PLDA, díky
čemuž je celý systém velmi robustní vůči různým mluvčím a akustickým
podmínkám. Model navíc dokáže velmi přesně zjistit počet mluvčích v
rozhovorech, jak ukazují výsledky na několika datových sadách. V době
zveřejnění dosáhl tento přístup nejlepších výsledků na třech relevantních
souborech dat: Callhome, AMI a DIHARD 2. VBx byl základním modulem našeho
vítězného systému v rámci druhé výzvy DIHARD a systémem, který se umístil na
druhém místě v soutěži VoxSRC 2020. Díky silným výsledkům a veřejným receptům,
které jsme zveřejnili, byl VBx použit jako výchozí systém organizátory
nedávných výzev, jako jsou M2MeT a Ego4D, a jako výchozí systém v bezpočtu
publikací. Zatímco kaskádové metody jsou poněkud těžkopádné kvůli nutnosti
aplikovat různé subsystémy jeden po druhém, poměrně často jsou výsledky
přístupů založených na shlukování, jako je VBx, prezentovány ve zjednodušených
nastaveních, jako je použití detekce hlasové aktivity nebo bez zpracování
překrývající se řeči. Za účelem poskytnutí spravedlivých referenčních hodnot
pro komunitu byl v této práci vyhodnocen celoplošný systém založený na VBx na
více než deseti různých souborech dat, přičemž u většiny z nich byly prokázány
silné schopnosti. Pro odstranění hlavních nevýhod modulárních systémů se
zbytek práce zaměřuje na modely neuronové diarizace end-to-end (EEND). Tyto
modely představují výhodu v tom, že všechny aspekty úlohy, které v modulárních
systémech zpracovávají různé a obvykle nezávislé systémy, řeší jediný modul.
End-to-end modely však vyžadují velké množství trénovacích dat a vzhledem k
nedostatku ručně anotovaných diarizačních dat v těchto množstvích je třeba
vytvářet data syntetická. Představujeme alternativní přístup k původnímu
schématu ,,simulovaných směsí” pro generování ,,simulovaných konverzací”.
Simulované směsi byly inspirovány technikami běžně používanými k přípravě
trénovacích dat pro separaci řeči, která obsahují vysoké množství překrývající
se řeči. To však neodráží vzorce v běžných rozhovorech. Abychom to vyřešili,
jsou naše simulované konverzace navrženy s využitím statistik ze skutečných
konverzací o délkách pauz a překrývání. Tato strategie přesněji napodobuje
skutečné interakce a při použití jako trénovacích dat pro end-to-end modely
jim umožňuje dosáhnout lepšího výkonu. Rozsáhlá srovnání našeho přístupu s
předchozím ukázala, že použití simulovaných konverzací téměř zcela snižuje
potřebu dolaďování modelu pomocí dat v doméně ve scénáři telefonování.
Zveřejnili jsme kód pro generování trénovacích dat, jakož i naši implementaci
modelu EEND s atraktory kodéru a dekodéru (EEND-EDA) v jazyce PyTorch a modely
natrénované na volných veřejných datech. Díky tomu byly simulované rozhovory
již převzaty do dalších publikací. Dále představujeme nový end-to-end model
DiaPer, který představuje zobecněný systém pro neautoregresivní dekódování
atraktorů v kontextu EEND. Ukazujeme, že může dosáhnout konkurenceschopných
výsledků na telefonních hovorech s menším počtem parametrů než jiné modely
EEND. Hlavními výhodami tohoto přístupu je lepší zvládání konverzací s mnoha
mluvčími a také lepší schopnost zpracovávat překrývající se řeč ve srovnání se
základním modelem EEND-EDA. DiaPer také vyhodnocujeme na několika
širokopásmových datových sadách, které poskytují komplexní výchozí údaje pro
budoucí publikace a na některých z nich vykazují velmi konkurenceschopný
výkon. Kód a modely natrénované na volně přístupných veřejných datech jsou
rovněž otevřeny. Nakonec porovnáváme systémy založené na VBx a DiaPer na
široké škále korpusů a komentujeme výhody každé techniky vzhledem ke
zvláštnostem každého souboru dat. Celkově si modulární systémy vedou lépe na
konverzacích s vysokým počtem mluvčích. End-to-end systémy mají výhodu při
práci s překryvy a nahrávkami s malým počtem mluvčích, avšak s jednostupňovým
inferenčním procesem. , extabstract.odd=true, faculty.cs=Fakulta informačních
technologií, faculty.en=Faculty of Information Technology, department.cs=Ústav
počítačové grafiky a multimédií, department.en=Department of Computer Graphics
and Multimedia

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[chapters]