Deep Learning Based Speech Quality Prediction

171 Pages·2022·7.372 MB·English

by  Gabriel Mittag

T-Labs Series in Telecommunication Services
Gabriel Mittag
Deep Learning
Based Speech
Quality
Prediction
T-Labs Series in Telecommunication Services
SeriesEditors
SebastianMo¨ller,QualityandUsabilityLab,TechnischeUniversitätBerlin,Berlin,
Germany
Axel Ku¨pper, Telekom Innovation Laboratories, Technische Universität Berlin,
Berlin,Germany
AlexanderRaake,AudiovisualTechnologyGroup,TechnischeUniversitätIlmenau,
Ilmenau,Germany
It is the aim of the Springer Series in Telecommunication Services to foster an
interdisciplinary exchange of knowledge addressing all topics which are essential
for developing high-quality and highly usable telecommunication services. This
includes basic concepts of underlying technologies, distribution networks, archi-
tectures and platforms for service design, deployment and adaptation, as well as
the users’perception of telecommunication services.By taking avertical perspec-
tiveoverallthesesteps,weaimtoprovidethescientificbasesforthedevelopment
and continuous evaluation of innovative services which provide a better value for
their users. In fact, the human-centric design of high-quality telecommunication
services – the so called “quality engineering” – forms an essential topic of this
series,asitwillultimatelyleadtobetteruserexperienceandacceptance.Theseries
isdirectedtowardsbothscientistsandpractitionersfromallrelateddisciplinesand
industries.
**Indexing:booksinthisseriesareindexinginScopus**
Moreinformationaboutthisseriesathttps://link.springer.com/bookseries/10013
Gabriel Mittag
Deep Learning Based Speech
Quality Prediction
GabrielMittag
TechnischeUniversita¨tBerlin
Berlin,Germany
ISSN2192-2810 ISSN2192-2829 (electronic)
T-LabsSeriesinTelecommunicationServices
ISBN978-3-030-91478-3 ISBN978-3-030-91479-0 (eBook)
https://doi.org/10.1007/978-3-030-91479-0
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Preface
Instrumentalspeechqualitypredictionisalong-studiedfieldinwhichmanymodels
have been presented. However, in particular, the single-ended prediction without
the use of a clean reference signal remains challenging. This book studies how
recent developments in machine learning can be leveraged to improve the quality
prediction of transmitted speech and additionally provide diagnostic information
through the prediction of speech quality dimensions. In particular, different deep
learning architectures were analyzed towards their suitability to predict speech
quality. To this end, a large dataset with distorted speech files and crowdsourced
subjective ratings was created. A number of deep learning architectures, such as
CNNs, LSTM networks, and Transformer/self-attention networks, were combined
andcompared.ItwasfoundthatanetworkwithCNN,self-attention,andaproposed
attention-pooling delivers the best single-ended speech quality predictions on the
considered dataset. Furthermore, a double-ended speech quality prediction model
basedonaSiameseneuralnetworkispresented.However,itcouldbeshownthat,
in contrast to traditional models, deep learning models only slightly benefit from
includingthecleanreferencesignal.Forthepredictionofperceptualspeechquality
dimensions,amulti-tasklearningbasedmodelispresentedthatpredictstheoverall
speech quality and the quality dimensions noisiness, coloration, discontinuity, and
loudnessinparallel,wheremostoftheneuralnetworklayersaresharedbetweenthe
individualtasks.Finally,thesingle-endedspeechqualitypredictionmodelNISQA
ispresentedthatwastrainedonalargevarietyof59differentdatasets.Becausethe
trainingdatasetscomefromavarietyofsourcesandcontaindifferentqualityranges,
they are exposed to subjective biases. Therefore, the same speech distortions can
leadtoverydifferentqualityratingsintwodatasets.Topreventanegativeinfluence
of this effect, a bias-aware loss function is proposed that estimates and considers
the biases during the training of the neural network weights. The final model was
testedonalive-talkingtestsetwithrealrecordedphonecalls,onwhichitachieved
aPearson’scorrelationof0.90fortheoverallspeechqualityprediction.
Berlin,Germany GabrielMittag
v
Acknowledgments
I am very grateful to the many supporters who have made this work possible.
Duringthelastyears,Ihadthepleasuretomeetandgettoknowmanyinteresting
peopleattheQualityandUsabilityLab,butalsoatseveralacademicconferences,
workshops, and ITU meetings. First, I would like to thank my thesis supervisor
Prof. Dr. Sebastian Möller for his support, his scientific expertise, and his advice
thatgreatlyhelpedmetowriteandcompletethisbook.Myspecialthanksalsogo
toDr.FriedemannKöster,whointroducedmetotheexcitingfieldofspeechquality
estimationandwithoutwhomIprobablywouldnothavestartedmydoctoralstudies.
IwouldliketothankmystudentassistantLouisLiedtkeforhissupportandalsoall
thestudentsIhadthepleasuretosuperviseduringtheirbachelor’sormaster’stheses
preparation,inparticularAssmaaChehadiforherworkononeofthedatasetsused
in this book and Huahua Maier on his work on the Android recording app. I also
wanttothankProf.Dr.GerhardSchmidtandTobiasHübschenfromtheUniversity
of Kiel and Dr. Jens Berger for the great collaboration during the DFG project.
I would like to thank Prof. Tiago H. Falk. and, a second time, Prof. Dr. Gerhard
Schmidtforreviewingthisbookandforservingonmydoctoralcommittee.Many
thanks go to Irene Hube-Achter, Yasmin Hillebrenner, and Tobias Jettkowski for
their excellent administrative and technical support. Thanks to all my former and
current colleagues at the Quality and Usability Lab for the numerous discussions,
exchangeofresearchideas,andforkeepingmecompanyduringmycoffeebreaks
and making sure that it never got too boring at the lab, including Steven Schmidt,
SamanZadtootaghaj,SaiSirishaRallabandi,ThiloMichael,TanjaKojic,Dr.Babak
Naderi,Dr.LauraFernándezGallardo,Dr.PatrickEhrenbrink,Dr.DennisGuse,Dr.
MaijaPoikela,Dr.FalkSchiffner,andDr.StefanUhrig,andmanymore.Thankyou
allforagreattime!
vii
Contents
1 Introduction .................................................................. 1
1.1 Motivation............................................................... 1
1.2 ThesisObjectivesandResearchQuestions............................ 4
1.3 Outline................................................................... 4
2 QualityAssessmentofTransmittedSpeech............................... 7
2.1 SpeechCommunicationNetworks..................................... 7
2.2 SpeechQualityandSpeechQualityDimensions...................... 10
2.3 SubjectiveAssessment.................................................. 12
2.4 SubjectiveAssessmentviaCrowdsourcing............................ 16
2.5 TraditionalInstrumentalMethods...................................... 18
2.5.1 ParametricModels.............................................. 18
2.5.2 Double-EndedSignal-BasedModels .......................... 19
2.5.3 Single-EndedSignal-BasedModels ........................... 21
2.6 MachineLearningBasedInstrumentalMethods ...................... 22
2.6.1 Non-DeepLearningMachineLearningApproaches.......... 23
2.6.2 DeepLearningArchitectures................................... 24
2.6.3 DeepLearningBasedSpeechQualityModels................ 28
2.7 Summary ................................................................ 31
3 NeuralNetworkArchitecturesforSpeechQualityPrediction.......... 33
3.1 Dataset................................................................... 33
3.1.1 SourceFiles..................................................... 34
3.1.2 SimulatedDistortions........................................... 35
3.1.3 LiveDistortions................................................. 39
3.1.4 ListeningExperiment........................................... 40
3.2 OverviewofNeuralNetworkModel................................... 42
3.3 Mel-SpecSegmentation ................................................ 43
3.4 FramewiseModel....................................................... 43
3.4.1 CNN............................................................. 43
3.4.2 FeedforwardNetwork .......................................... 45
ix
x Contents
3.5 Time-DependencyModelling .......................................... 46
3.5.1 LSTM ........................................................... 47
3.5.2 Transformer/Self-Attention .................................... 48
3.6 TimePooling............................................................ 51
3.6.1 Average-/Max-Pooling ......................................... 51
3.6.2 Last-Step-Pooling............................................... 51
3.6.3 Attention-Pooling............................................... 52
3.7 ExperimentsandResults................................................ 53
3.7.1 TrainingandEvaluationMetric................................ 53
3.7.2 FramewiseModel............................................... 54
3.7.3 Time-DependencyModel ...................................... 56
3.7.4 PoolingModel .................................................. 57
3.8 Summary ................................................................ 58
4 Double-EndedSpeechQualityPredictionUsingSiameseNetworks... 59
4.1 Introduction ............................................................. 59
4.2 Method................................................................... 60
4.2.1 SiameseNeuralNetwork....................................... 62
4.2.2 ReferenceAlignment........................................... 62
4.2.3 FeatureFusion .................................................. 64
4.3 Results................................................................... 65
4.3.1 LSTMvsSelf-Attention........................................ 65
4.3.2 Alignment....................................................... 66
4.3.3 FeatureFusion .................................................. 67
4.3.4 Double-EndedvsSingle-Ended................................ 68
4.4 Summary ................................................................ 70
5 PredictionofSpeechQualityDimensionswithMulti-Task
Learning ...................................................................... 73
5.1 Introduction ............................................................. 73
5.2 Multi-TaskModels...................................................... 75
5.2.1 FullyConnected(MTL-FC).................................... 76
5.2.2 FullyConnected+Pooling(MTL-POOL) .................... 77
5.2.3 FullyConnected+Pooling+Time-Dependency
(MTL-TD)....................................................... 78
5.2.4 FullyConnected+Pooling+Time-Dependency+
CNN(MTL-CNN).............................................. 79
5.3 Results................................................................... 79
5.3.1 Per-TaskEvaluation ............................................ 80
5.3.2 All-TasksEvaluation ........................................... 83
5.3.3 ComparingDimension ......................................... 84
5.3.4 DegradationDecomposition.................................... 85
5.4 Summary ................................................................ 87
Contents xi
6 Bias-AwareLossforTrainingfromMultipleDatasets................... 89
6.1 Method................................................................... 90
6.1.1 LearningwithBias-AwareLoss................................ 91
6.1.2 AnchoringPredictions.......................................... 92
6.2 ExperimentsandResults................................................ 93
6.2.1 SyntheticData .................................................. 94
6.2.2 MinimumAccuracyr ......................................... 95
th
6.2.3 TrainingExampleswithandWithoutAnchoring ............. 96
6.2.4 ConfigurationComparisons.................................... 97
6.2.5 SpeechQualityDataset......................................... 99
6.3 Summary ................................................................ 100
7 NISQA:ASingle-EndedSpeechQualityModel.......................... 103
7.1 Datasets.................................................................. 103
7.1.1 POLQAPool.................................................... 104
7.1.2 ITU-TPSuppl.23.............................................. 104
7.1.3 OtherDatasets .................................................. 106
7.1.4 Live-TalkingTestSet........................................... 109
7.2 ModelandTraining..................................................... 110
7.2.1 Model............................................................ 110
7.2.2 Bias-AwareLoss................................................ 111
7.2.3 HandlingMissingDimensionRatings......................... 111
7.2.4 Training ......................................................... 112
7.3 Results................................................................... 114
7.3.1 EvaluationMetrics.............................................. 115
7.3.2 ValidationSetResults:OverallQuality ....................... 116
7.3.3 ValidationSetResults:QualityDimensions................... 118
7.3.4 TestSetResults................................................. 122
7.3.5 ImpairmentLevelvsQualityPrediction....................... 125
7.4 Summary ................................................................ 138
8 Conclusions................................................................... 141
A DatasetConditionTables.................................................... 145
B TrainandValidationDatasetDimensionHistograms ................... 149
References......................................................................... 153
Index............................................................................... 161