Perkeptuaalinen spektrisovitus glottisherätevokoodatussa tilastollisessa parametrisessa puhesynteesissä käyttäen mel-suodinpankkia

This thesis presents a novel perceptual spectral matching technique for parametric statistical speech synthesis with glottal vocoding. The proposed method utilizes a perceptual matching criterion based on mel-scale filterbanks. The background section discusses the physiology and modelling of human speech production and perception, necessary for speech synthesis and perceptual spectral matching. Additionally, the working principles of statistical parametric speech synthesis and the baseline glottal source excited vocoder are described. The proposed method is evaluated by comparing it to the baseline method first by an objective measure based on the mel-cepstral distance, and second by a subjective listening test. The novel method was found to give comparable performance to the baseline spectral matching method of the glottal vocoder.Tämä työ esittää uuden perkeptuaalisen spektrisovitustekniikan glottisvokoodattua tilastollista parametristä puhesynteesiä varten. Ehdotettu menetelmä käyttää mel-suodinpankkeihin perustuvaa perkeptuaalista sovituskriteeriä. Työn taustaosuus käsittelee ihmisen puheentuoton ja havaitsemisen fysiologiaa ja mallintamista tilastollisen parametrisen puhesynteesin ja perkeptuaalisen spektrisovituksen näkökulmasta. Lisäksi kuvataan tilastollisen parametrisen puhesynteesin ja perusmuotoisen glottisherätevokooderin toimintaperiaatteet. Uutta menetelmää arvioidaan vertaamalla sitä alkuperäiseen metodiin ensin käyttämällä mel-kepstrikertoimia käyttävää objektiivista etäisyysmittaa ja toiseksi käyttäen subjektiivisia kuuntelukokeita. Uuden metodin havaittiin olevan laadullisesti samalla tasolla alkuperäisen spektrisovitusmenetelmän kanssa

Adversarial Guitar Amplifier Modelling With Unpaired Data

We propose an audio effects processing framework that learns to emulate a target electric guitar tone from a recording. We train a deep neural network using an adversarial approach, with the goal of transforming the timbre of a guitar, into the timbre of another guitar after audio effects processing has been applied, for example, by a guitar amplifier. The model training requires no paired data, and the resulting model emulates the target timbre well whilst being capable of real-time processing on a modern personal computer. To verify our approach we present two experiments, one which carries out unpaired training using paired data, allowing us to monitor training via objective metrics, and another that uses fully unpaired data, corresponding to a realistic scenario where a user wants to emulate a guitar timbre only using audio data from a recording. Our listening test results confirm that the models are perceptually convincing

Reducing mismatch in training of DNN-based glottal excitation models in a statistical parametric text-to-speech system

Neural network-based models that generate glottal excitation waveforms from acoustic features have been found to give improved quality in statistical parametric speech synthesis. Until now, however, these models have been trained separately from the acoustic model. This creates mismatch between training and synthesis, as the synthesized acoustic features used for the excitation model input differ from the original inputs, with which the model was trained on. Furthermore, due to the errors in predicting the vocal tract filter, the original excitation waveforms do not provide perfect reconstruction of the speech waveform even if predicted without error. To address these issues and to make the excitation model more robust against errors in acoustic modeling, this paper proposes two modifications to the excitation model training scheme. First, the excitation model is trained in a connected manner, with inputs generated by the acoustic model. Second, the target glottal waveforms are re-estimated by performing glottal inverse filtering with the predicted vocal tract filters. The results show that both of these modifications improve performance measured in MSE and MFCC distortion, and slightly improve the subjective quality of the synthetic speech.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

Speaker-independent neural formant synthesis

We describe speaker-independent speech synthesis driven by a small set of phonetically meaningful speech parameters such as formant frequencies. The intention is to leverage deep-learning advances to provide a highly realistic signal generator that includes control affordances required for stimulus creation in the speech sciences. Our approach turns input speech parameters into predicted mel-spectrograms, which are rendered into waveforms by a pre-trained neural vocoder. Experiments with WaveNet and HiFi-GAN confirm that the method achieves our goals of accurate control over speech parameters combined with high perceptual audio quality. We also find that the small set of phonetically relevant speech parameters we use is sufficient to allow for speaker-independent synthesis (a.k.a. universal vocoding).Comment: 5 pages, 4 figures. Article accepted at INTERSPEECH 202

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