Exploring efficient neural architectures for linguistic-acoustic mapping in text-to-speech

Conversion from text to speech relies on the accurate mapping from linguistic to acoustic symbol sequences, for which current practice employs recurrent statistical models such as recurrent neural networks. Despite the good performance of such models (in terms of low distortion in the generated speech), their recursive structure with intermediate affine transformations tends to make them slow to train and to sample from. In this work, we explore two different mechanisms that enhance the operational efficiency of recurrent neural networks, and study their performance–speed trade-off. The first mechanism is based on the quasi-recurrent neural network, where expensive affine transformations are removed from temporal connections and placed only on feed-forward computational directions. The second mechanism includes a module based on the transformer decoder network, designed without recurrent connections but emulating them with attention and positioning codes. Our results show that the proposed decoder networks are competitive in terms of distortion when compared to a recurrent baseline, whilst being significantly faster in terms of CPU and GPU inference time. The best performing model is the one based on the quasi-recurrent mechanism, reaching the same level of naturalness as the recurrent neural network based model with a speedup of 11.2 on CPU and 3.3 on GPU.Peer ReviewedPostprint (published version

An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation

Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources. Traditionally, these tasks have been tackled using signal processing and machine learning techniques applied to the available acoustic signals. Since the visual aspect of speech is essentially unaffected by the acoustic environment, visual information from the target speakers, such as lip movements and facial expressions, has also been used for speech enhancement and speech separation systems. In order to efficiently fuse acoustic and visual information, researchers have exploited the flexibility of data-driven approaches, specifically deep learning, achieving strong performance. The ceaseless proposal of a large number of techniques to extract features and fuse multimodal information has highlighted the need for an overview that comprehensively describes and discusses audio-visual speech enhancement and separation based on deep learning. In this paper, we provide a systematic survey of this research topic, focusing on the main elements that characterise the systems in the literature: acoustic features; visual features; deep learning methods; fusion techniques; training targets and objective functions. In addition, we review deep-learning-based methods for speech reconstruction from silent videos and audio-visual sound source separation for non-speech signals, since these methods can be more or less directly applied to audio-visual speech enhancement and separation. Finally, we survey commonly employed audio-visual speech datasets, given their central role in the development of data-driven approaches, and evaluation methods, because they are generally used to compare different systems and determine their performance

FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis

Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hindered their applications to speech synthesis. This paper proposes FastDiff, a fast conditional diffusion model for high-quality speech synthesis. FastDiff employs a stack of time-aware location-variable convolutions of diverse receptive field patterns to efficiently model long-term time dependencies with adaptive conditions. A noise schedule predictor is also adopted to reduce the sampling steps without sacrificing the generation quality. Based on FastDiff, we design an end-to-end text-to-speech synthesizer, FastDiff-TTS, which generates high-fidelity speech waveforms without any intermediate feature (e.g., Mel-spectrogram). Our evaluation of FastDiff demonstrates the state-of-the-art results with higher-quality (MOS 4.28) speech samples. Also, FastDiff enables a sampling speed of 58x faster than real-time on a V100 GPU, making diffusion models practically applicable to speech synthesis deployment for the first time. We further show that FastDiff generalized well to the mel-spectrogram inversion of unseen speakers, and FastDiff-TTS outperformed other competing methods in end-to-end text-to-speech synthesis. Audio samples are available at \url{https://FastDiff.github.io/}.Comment: Accepted by IJCAI 202

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

Voice Spoofing Countermeasures: Taxonomy, State-of-the-art, experimental analysis of generalizability, open challenges, and the way forward

Malicious actors may seek to use different voice-spoofing attacks to fool ASV systems and even use them for spreading misinformation. Various countermeasures have been proposed to detect these spoofing attacks. Due to the extensive work done on spoofing detection in automated speaker verification (ASV) systems in the last 6-7 years, there is a need to classify the research and perform qualitative and quantitative comparisons on state-of-the-art countermeasures. Additionally, no existing survey paper has reviewed integrated solutions to voice spoofing evaluation and speaker verification, adversarial/antiforensics attacks on spoofing countermeasures, and ASV itself, or unified solutions to detect multiple attacks using a single model. Further, no work has been done to provide an apples-to-apples comparison of published countermeasures in order to assess their generalizability by evaluating them across corpora. In this work, we conduct a review of the literature on spoofing detection using hand-crafted features, deep learning, end-to-end, and universal spoofing countermeasure solutions to detect speech synthesis (SS), voice conversion (VC), and replay attacks. Additionally, we also review integrated solutions to voice spoofing evaluation and speaker verification, adversarial and anti-forensics attacks on voice countermeasures, and ASV. The limitations and challenges of the existing spoofing countermeasures are also presented. We report the performance of these countermeasures on several datasets and evaluate them across corpora. For the experiments, we employ the ASVspoof2019 and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifiers. (For reproduceability of the results, the code of the test bed can be found in our GitHub Repository

Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion

Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although solutions are mutually dependent, they have typically evolved as standalone sub-systems whereby CM solutions are usually designed for a fixed ASV system. The work reported in this paper aims to gauge the improvements in reliability that can be gained from their closer integration. Results derived using the popular ASVspoof2019 dataset indicate that the equal error rate (EER) of a state-of-the-art ASV system degrades from 1.63% to 23.83% when the evaluation protocol is extended with spoofed trials.%subjected to spoofing attacks. However, even the straightforward integration of ASV and CM systems in the form of score-sum and deep neural network-based fusion strategies reduce the EER to 1.71% and 6.37%, respectively. The new Spoofing-Aware Speaker Verification (SASV) challenge has been formed to encourage greater attention to the integration of ASV and CM systems as well as to provide a means to benchmark different solutions.Comment: 8 pages, accepted by Odyssey 202

Replay detection in voice biometrics: an investigation of adaptive and non-adaptive front-ends

Among various physiological and behavioural traits, speech has gained popularity as an effective mode of biometric authentication. Even though they are gaining popularity, automatic speaker verification systems are vulnerable to malicious attacks, known as spoofing attacks. Among various types of spoofing attacks, replay attack poses the biggest threat due to its simplicity and effectiveness. This thesis investigates the importance of 1) improving front-end feature extraction via novel feature extraction techniques and 2) enhancing spectral components via adaptive front-end frameworks to improve replay attack detection. This thesis initially focuses on AM-FM modelling techniques and their use in replay attack detection. A novel method to extract the sub-band frequency modulation (FM) component using the spectral centroid of a signal is proposed, and its use as a potential acoustic feature is also discussed. Frequency Domain Linear Prediction (FDLP) is explored as a method to obtain the temporal envelope of a speech signal. The temporal envelope carries amplitude modulation (AM) information of speech resonances. Several features are extracted from the temporal envelope and the FDLP residual signal. These features are then evaluated for replay attack detection and shown to have significant capability in discriminating genuine and spoofed signals. Fusion of AM and FM-based features has shown that AM and FM carry complementary information that helps distinguish replayed signals from genuine ones. The importance of frequency band allocation when creating filter banks is studied as well to further advance the understanding of front-ends for replay attack detection. Mechanisms inspired by the human auditory system that makes the human ear an excellent spectrum analyser have been investigated and integrated into front-ends. Spatial differentiation, a mechanism that provides additional sharpening to auditory filters is one of them that is used in this work to improve the selectivity of the sub-band decomposition filters. Two features are extracted using the improved filter bank front-end: spectral envelope centroid magnitude (SECM) and spectral envelope centroid frequency (SECF). These are used to establish the positive effect of spatial differentiation on discriminating spoofed signals. Level-dependent filter tuning, which allows the ear to handle a large dynamic range, is integrated into the filter bank to further improve the front-end. This mechanism converts the filter bank into an adaptive one where the selectivity of the filters is varied based on the input signal energy. Experimental results show that this leads to improved spoofing detection performance. Finally, deep neural network (DNN) mechanisms are integrated into sub-band feature extraction to develop an adaptive front-end that adjusts its characteristics based on the sub-band signals. A DNN-based controller that takes sub-band FM components as input, is developed to adaptively control the selectivity and sensitivity of a parallel filter bank to enhance the artifacts that differentiate a replayed signal from a genuine signal. This work illustrates gradient-based optimization of a DNN-based controller using the feedback from a spoofing detection back-end classifier, thus training it to reduce spoofing detection error. The proposed framework has displayed a superior ability in identifying high-quality replayed signals compared to conventional non-adaptive frameworks. All techniques proposed in this thesis have been evaluated on well-established databases on replay attack detection and compared with state-of-the-art baseline systems