Analysis/Synthesis Comparison of Vocoders Utilized in Statistical Parametric Speech Synthesis

Tässä työssä esitetään kirjallisuuskatsaus ja kokeellinen osio tilastollisessa parametrisessa puhesynteesissä käytetyistä vokoodereista. Kokeellisessa osassa kolmen valitun vokooderin (GlottHMM, STRAIGHT ja Harmonic/Stochastic Model) analyysi-synteesi -ominaisuuksia tarkastellaan usealla tavalla. Suoritetut kokeet olivat vokooderiparametrien tilastollisten jakaumien analysointi, puheen tunnetilan tilastollinen vaikutus vokooderiparametrien jakaumiin sekä subjektiivinen kuuntelukoe jolla mitattiin vokooderien suhteellista analyysi-synteesi -laatua. Tulokset osoittavat että STRAIGHT-vokooderi omaa eniten Gaussiset parametrijakaumat ja tasaisimman synteesilaadun. GlottHMM-vokooderin parametrit osoittivat suurinta herkkyyttä puheen tunnetilan funktiona ja vokooderi sai parhaan, mutta laadultaan vaihtelevan kuuntelukoetuloksen. HSM-vokooderin LSF-parametrien havaittiin olevan Gaussisempia kuin GlottHMM-vokooderin LSF parametrit, mutta vokooderin havaittiin kärsivän kohinaherkkyydestä, ja se sai huonoimman kuuntelukoetuloksen.This thesis presents a literature study followed by an experimental part on the state-of-the-art vocoders utilized in statistical parametric speech synthesis. In the experimental part, the analysis/synthesis properties of three selected vocoders (GlottHMM, STRAIGHT and Harmonic/Stochastic Model) are examined. The performed tests were the analysis of vocoder parameter distributions, statistical testing on the effect of emotions to the vocoder parameter distributions, and a subjective listening test evaluating the vocoders' relative analysis/synthesis quality. The results indicate that the STRAIGHT vocoder has the most Gaussian parameter distributions and most robust synthesis quality, whereas the GlottHMM vocoder has the most emotion sensitive parameters and best but unreliable synthesis quality. The HSM vocoder's LSF parameters were found to be more Gaussian than the GlottHMM vocoder's LSF parameters. HSM was found to be sensitive to noise, and it scored the lowest score on the subjective listening test

Comparison of End-to-End Neural Network Architectures and Data Augmentation Methods for Automatic Infant Motility Assessment Using Wearable Sensors

Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis plays a central role. This study investigates the use of different end-to-end neural network architectures for processing infant motility data from wearable sensors. We focus on the performance and computational burden of alternative sensor encoder and time series modeling modules and their combinations. In addition, we explore the benefits of data augmentation methods in ideal and nonideal recording conditions. The experiments are conducted using a dataset of multisensor movement recordings from 7-month-old infants, as captured by a recently proposed smart jumpsuit for infant motility assessment. Our results indicate that the choice of the encoder module has a major impact on classifier performance. For sensor encoders, the best performance was obtained with parallel two-dimensional convolutions for intrasensor channel fusion with shared weights for all sensors. The results also indicate that a relatively compact feature representation is obtainable for within-sensor feature extraction without a drastic loss to classifier performance. Comparison of time series models revealed that feedforward dilated convolutions with residual and skip connections outperformed all recurrent neural network (RNN)-based models in performance, training time, and training stability. The experiments also indicate that data augmentation improves model robustness in simulated packet loss or sensor dropout scenarios. In particular, signal- and sensor-dropout-based augmentation strategies provided considerable boosts to performance without negatively affecting the baseline performance. Overall, the results provide tangible suggestions on how to optimize end-to-end neural network training for multichannel movement sensor data

Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition

Funding Information: This research was funded by Academy of Finland grants no. 314602, 335872, and 343498. Publisher Copyright: Copyright © 2022 ISCA.When domain experts are needed to perform data annotation for complex machine-learning tasks, reducing annotation effort is crucial in order to cut down time and expenses. For cases when there are no annotations available, one approach is to utilize the structure of the feature space for clustering-based active learning (AL) methods. However, these methods are heavily dependent on how the samples are organized in the feature space and what distance metric is used. Unsupervised methods such as contrastive predictive coding (CPC) can potentially be used to learn organized feature spaces, but these methods typically create high-dimensional features which might be challenging for estimating data density. In this paper, we combine CPC and multiple dimensionality reduction methods in search of functioning practices for clustering-based AL. Our experiments for simulating speech emotion recognition system deployment show that both the local and global topology of the feature space can be successfully used for AL, and that CPC can be used to improve clustering-based AL performance over traditional signal features. Additionally, we observe that compressing data dimensionality does not harm AL performance substantially, and that 2-D feature representations achieved similar AL performance as higher-dimensional representations when the number of annotations is not very low.Peer reviewe

A Comparison Between STRAIGHT, Glottal, an Sinusoidal Vocoding in Statistical Parametric Speech Synthesis

Speech is a fundamental method of human communication that allows conveying information between people. Even though the linguistic content is commonly regarded as the main information in speech, the signal contains a richness of other information, such as prosodic cues that shape the intended meaning of a sentence. This information is largely generated by quasi-periodic glottal excitation, which is the acoustic speech excitation airflow originating from the lungs that makes the vocal folds oscillate in the production of voiced speech. By regulating the sub-glottal pressure and the tension of the vocal folds, humans learn to affect the characteristics of the glottal excitation in order to signal the emotional state of the speaker for example. Glottal inverse filtering (GIF) is an estimation method for the glottal excitation of a recorded speech signal. Various cues about the speech signal, such as the mode of phonation, can be detected and analyzed from an estimate of the glottal flow, both instantaneously and as a function of time. Aside from its use in fundamental speech research, such as phonetics, the recent advances in GIF and machine learning enable a wider variety of GIF applications, such as emotional speech synthesis and the detection of paralinguistic information. However, GIF is a difficult inverse problem where the target algorithm output is generally unattainable with direct measurements. Thus the algorithms and their evaluation need to rely on some prior assumptions about the properties of the speech signal. A common thread utilized in most of the studies in this thesis is the estimation of the vocal tract transfer function (the key problem in GIF) by temporally weighting the optimization criterion in GIF so that the effect of the main excitation peak is attenuated. This thesis studies GIF from various perspectives---including the development of two new GIF methods that improve GIF performance over the state-of-the-art methods---and furthers basic research in the automated estimation of glottal excitation. The estimation of the GIF-based vocal tract transfer function for formant tracking and perceptually weighted speech envelope estimation is also studied. The central speech technology application of GIF addressed in the thesis is the use of GIF-based spectral envelope models and glottal excitation waveforms as target training data for the generative neural network models used in statistical parametric speech synthesis. The obtained results show that even though the presented studies provide improvements to the previous methodology for all voice types, GIF-based speech processing continues to mainly benefit male voices in speech synthesis applications.Puhe on olennainen osa ihmistenvälistä informaation siirtoa. Vaikka kielellistä sisältöä pidetään yleisesti puheen tärkeimpänä ominaisuutena, puhesignaali sisältää myös runsaasti muuta informaatiota kuten prosodisia vihjeitä, jotka muokkaavat siirrettävän informaation merkitystä. Tämä informaatio tuotetaan suurilta osin näennäisjaksollisella glottisherätteellä, joka on puheen herätteenä toimiva akustinen virtaussignaali. Säätämällä äänihuulten alapuolista painetta ja äänihuulten kireyttä ihmiset muuttavat glottisherätteen ominaisuuksia viestittääkseen esimerkiksi tunnetilaa. Glottaalinen käänteissuodatus (GKS) on laskennallinen menetelmä glottisherätteen estimointiin nauhoitetusta puhesignaalista. Glottisherätteen perusteella puheen laadusta voidaan tunnistaa useita piirteitä kuten ääntötapa, sekä hetkellisesti että ajan funktiona. Puheen perustutkimuksen, kuten fonetiikan, lisäksi viimeaikaiset edistykset GKS:ssä ja koneoppimisessa ovat avaamassa mahdollisuuksia laajempaan GKS:n soveltamiseen puheteknologiassa, kuten puhesynteesissä ja puheen biopiirteistämisessä paralingvistisiä sovelluksia varten. Haasteena on kuitenkin se, että GKS on vaikea käänteisongelma, jossa todellista puhetta vastaavan glottisherätteen suora mittaus on mahdotonta. Tästä johtuen GKS:ssä käytettävien algoritmien kehitystyö ja arviointi perustuu etukäteisoletuksiin puhesignaalin ominaisuuksista. Tässä väitöskirjassa esitetyissä menetelmissä on yhteisenä oletuksena se, että ääntöväylän siirtofunktio voidaan arvioida (joka on GKS:n pääongelma) aikapainottamalla GKS:n optimointikriteeriä niin, että glottisherätteen pääeksitaatiopiikkin vaikutus vaimenee. Tässä väitöskirjassa GKS:ta tutkitaan useasta eri näkökulmasta, jotka sisältävät kaksi uutta GKS-menetelmää, jotka parantavat arviointituloksia aikaisempiin menetelmiin verrattuna, sekä perustutkimusta käänteissuodatusprosessin automatisointiin liittyen. Lisäksi GKS-pohjaista ääntöväylän siirtofunktiota käytetään formanttiestimoinnissa sekä kuulohavaintopainotettuna versiona puheen spektrin verhokäyrän arvioinnissa. Tämän väitöskirjan keskeisin puheteknologiasovellus on GKS-pohjaisten puheen spektrin verhokäyrämallien sekä glottisheräteaaltomuotojen käyttö kohdedatana neuroverkkomalleille tilastollisessa parametrisessa puhesynteesissä. Saatujen tulosten perusteella kehitetyt menetelmät parantavat GKS-pohjaisten menetelmien laatua kaikilla äänityypeillä, mutta puhesynteesisovelluksissa GKS-pohjaiset ratkaisut hyödyttävät edelleen lähinnä matalia miesääniä

Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition

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Normal-to-Lombard Adaptation of Speech Synthesis Using Long Short-Term Memory Recurrent Neural Networks

In this article, three adaptation methods are compared based on how well they change the speaking style of a neural network based text-to-speech (TTS) voice. The speaking style conversion adopted here is from normal to Lombard speech. The selected adaptation methods are: auxiliary features (AF), learning hidden unit contribution (LHUC), and fine-tuning (FT). Furthermore, four state-of-the-art TTS vocoders are compared in the same context. The evaluated vocoders are: GlottHMM, GlottDNN, STRAIGHT, and pulse model in log-domain (PML). Objective and subjective evaluations were conducted to study the performance of both the adaptation methods and the vocoders. In the subjective evaluations, speaking style similarity and speech intelligibility were assessed. In addition to acoustic model adaptation, phoneme durations were also adapted from normal to Lombard with the FT adaptation method. In objective evaluations and speaking style similarity tests, we found that the FT method outperformed the other two adaptation methods. In speech intelligibility tests, we found that there were no significant differences between vocoders although the PML vocoder showed slightly better performance compared to the three other vocoders.Peer reviewe

Speaker-independent raw waveform model for glottal excitation

Recent speech technology research has seen a growing interest in using WaveNets as statistical vocoders, i.e., generating speech waveforms from acoustic features. These models have been shown to improve the generated speech quality over classical vocoders in many tasks, such as text-to-speech synthesis and voice conversion. Furthermore, conditioning WaveNets with acoustic features allows sharing the waveform generator model across multiple speakers without additional speaker codes. However, multi-speaker WaveNet models require large amounts of training data and computation to cover the entire acoustic space. This paper proposes leveraging the source-filter model of speech production to more effectively train a speaker-independent waveform generator with limited resources. We present a multi-speaker ’GlotNet’ vocoder, which utilizes a WaveNet to generate glottal excitation waveforms, which are then used to excite the corresponding vocal tract filter to produce speech. Listening tests show that the proposed model performs favourably to a direct WaveNet vocoder trained with the same model architecture and data.Peer reviewe

Alternating minimisation for glottal inverse filtering

A new method is proposed for solving the glottal inverse filtering (GIF) problem. The goal of GIF is to separate an acoustical speech signal into two parts: the glottal airflow excitation and the vocal tract filter. To recover such information one has to deal with a blind deconvolution problem. This ill-posed inverse problem is solved under a deterministic setting, considering unknowns on both sides of the underlying operator equation. A stable reconstruction is obtained using a double regularization strategy, alternating between fixing either the glottal source signal or the vocal tract filter. This enables not only splitting the nonlinear and nonconvex problem into two linear and convex problems, but also allows the use of the best parameters and constraints to recover each variable at a time. This new technique, called alternating minimization glottal inverse filtering (AM-GIF), is compared with two other approaches: Markov chain Monte Carlo glottal inverse filtering (MCMC-GIF), and iterative adaptive inverse filtering (IAIF), using synthetic speech signals. The recent MCMC-GIF has good reconstruction quality but high computational cost. The state-of-the-art IAIF method is computationally fast but its accuracy deteriorates, particularly for speech signals of high fundamental frequency (F0). The results show the competitive performance of the new method: With high F0, the reconstruction quality is better than that of IAIF and close to MCMC-GIF while reducing the computational complexity by two orders of magnitude.Peer reviewe

DATA AUGMENTATION STRATEGIES FOR NEURAL NETWORK F0 ESTIMATION

This study explores various speech data augmentation methods for the task of noise-robust fundamental frequency (F0) estimation with neural networks. The explored augmentation strategies are split into additive noise and channel-based augmentation and into vocoder-based augmentation methods. In vocoder-based augmentation, a glottal vocoder is used to enhance the accuracy of ground truth F0 used for training of the neural network, as well as to expand the training data diversity in terms of F0 patterns and vocal tract lengths of the talkers. Evaluations on the PTDB-TUG corpus indicate that noise and channel augmentation can be used to greatly increase the noise robustness of trained models, and that vocoder-based ground truth enhancement further increases model performance. For smaller datasets, vocoder-based diversity augmentation can also be used to increase performance. The best-performing proposed method greatly outperformed the compared F0 estimation methods in terms of noise robustness.Peer reviewe