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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Subramani, Krishna Rao, Preeti D'Hooge, Alexandre Indian Inst Technol Mumbai Maharashtra India ENS Paris Saclay Paris France

ISBN: (纸本)9781509066315

With the advent of data-driven statistical modeling and abundant computing power, researchers are turning increasingly to deep learning for audio synthesis. These methods try to model audio signals directly in the time or frequency domain. In the interest of more flexible control over the generated sound, it could be more useful to work with a parametric representation of the signal which corresponds more directly to the musical attributes such as pitch, dynamics and timbre. We present VaPar Synth - a Variational Parametric Synthesizer which utilizes a conditional variational autoencoder (CVAE) trained on a suitable parametric representation. We demonstrate(1) our proposed model's capabilities via the reconstruction and generation of instrumental tones with flexible control over their pitch.

关键词： Generative models Conditional VAE source-filter model Spectral modeling Synthesis

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On the Detection of Pitch-Shifted Voice: Machines and Human Listeners

On the Detection of Pitch-Shifted Voice: Machines and Human ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： David Looney Nikolay D. Gaubitch Pindrop Inc. London UK

We present a performance comparison between human listeners and a simple algorithm for the task of speech anomaly detection. The algorithm utilises an intentionally small set of features derived from the source-filter model, with the aim of validating that key components of source-filter theory characterise how humans perceive anomalies. We furthermore recognise that humans are adept at detecting anomalies without prior exposure to a given anomaly class. To that end, we also consider the algorithm performance when operating via the principle of unsupervised learning where a null model is derived from normal speech recordings. We evaluate both the algorithm and human listeners for pitch-shift detection where the pitch of a speech sample is intentionally modified using software, a phenomenon of relevance to the fields of fraud detection and forensics. Our results show that humans can only detect pitch-shift reliably at more extreme levels, and that the performance of the algorithm matches closely with that of humans.

关键词： fraud voice modification source-filter model

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A source-filter model FOR MUSICAL INSTRUMENT SOUND TRANSFORMATION

A SOURCE-FILTER MODEL FOR MUSICAL INSTRUMENT SOUND TRANSFORM...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Caetano, Marcelo Rodet, Xavier Analysis/Synthesis Team IRCAM France

ISBN: (纸本)9781467300469

The model used to represent musical instrument sounds plays a crucial role in the quality of sound transformations. Ideally, the representation should be compact and accurate, while its parameters should give flexibility to independently manipulate perceptually related features of the sounds. This work describes a source-filter model for musical instrument sounds based on the sinusoidal plus residual decomposition. The sinusoidal component is modeled as sinusoidal partial tracks (source) and a time-varying spectral envelope (filter), and the residual is represented as white noise (source) shaped by a time-varying spectral envelope (filter). This article presents estimation and representation techniques that give totally independent and intuitive control of the spectral envelope model and the frequencies of the partials to perform perceptually related sound transformations. The result of a listening test confirmed that, in general, the sounds resynthesized from the source-filter model are perceptually similar to the original recordings.

关键词： source-filter model sinusoidal models musical instrument timbre sound transformations

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GELP: GAN-Excited Linear Prediction for Speech Synthesis from Mel-spectrogram 20

GELP: GAN-Excited Linear Prediction for Speech Synthesis fro...

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Interspeech Conference

作者： Juvela, Lauri Bollepalli, Bajibabu Yamagishi, Junichi Alku, Paavo Aalto Univ Espoo Finland Natl Inst Informat Tokyo Japan Univ Edinburgh Edinburgh Midlothian Scotland

Recent advances in neural network-based text-to-speech have reached human level naturalness in synthetic speech. The present sequence-to-sequence models can directly map text to mel-spectrogram acoustic features, which are convenient for modeling, but present additional challenges for vocoding (i.e., waveform generation from the acoustic features). High-quality synthesis can be achieved with neural vocoders, such as WaveNet, but such autoregressive models suffer from slow sequential inference. Meanwhile, their existing parallel inference counterparts are difficult to train and require increasingly large model sizes. In this paper, we propose an alternative training strategy for a parallel neural vocoder utilizing generative adversarial networks, and integrate a linear predictive synthesis filter into the model. Results show that the proposed model achieves significant improvement in inference speed, while outperforming a WaveNet in copy-synthesis quality.

关键词： Neural vocoder source-filter model GAN WaveNet

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Voice quality enhancement for vocal tract rehabilitation 3

Voice quality enhancement for vocal tract rehabilitation

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3rd Biennial South African Biomedical Engineering Conference (SAIBMEC)

作者： Sutcliffe, Bianca Wiggins, Lindzi Rubin, David Aharonson, Vered Univ Witwatersrand Sch Elect & Informat Engn Biomed Engn Res Grp Johannesburg South Africa

ISBN: (纸本)9781538625163

Vocal rehabilitation devices used by patients after Laryngectomy produce an unnatural sounding speech. Our study aims at increasing the quality of these synthetically generated voices by implementing human-like characteristics. A simplified source filter model, linear predictive coding coefficients and line spectral frequencies were used to model the vocal tract and manipulate the acoustic features of their resulting speech. Two different mapping functions were employed to convert between the features of synthetically generated voice and those of a human voice: A Gaussian mixture model and a linear regression model. The models were trained on a set of 50 human and 50 synthetic voice utterances. Both mapping functions yielded significant changes in the transformed synthetic voices and their spectra were similar to the human voices. The linear regression model mapping produced slightly better results compared to the Gaussian mixture model mapping. Listeners' tests confirmed this result, but indicated that voices re-synthesized from the transformed model coefficients, improved on the synthetic voice but still sounded unnatural. This may imply that the vocal tract model is lacking in information that produces the subjective perception of "artificial speech". Future work will investigate an elaborate model which will include the speech production excitation and radiation signals and the transformation of their features. These models have the potential to improve the conversion of synthetically generated electrolarynx voice into human sounding one.

关键词： voice conversion source-filter model Gaussian mixture model linear regression linear predictive coding coefficients line spectral frequencies

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Glottal Vocoding With Frequency-Warped Time-Weighted Linear Prediction

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IEEE SIGNAL PROCESSING LETTERS 2017年第4期24卷 446-450页

作者： Airaksinen, Manu Bollepalli, Bajibabu Pohjalainen, Jouni Alku, Paavo Aalto Univ Dept Signal Proc & Acoust Espoo 02150 Finland Univ Passau D-94032 Passau Germany

Linear prediction (LP) is a prevalent source-filter separation method of speech production. One of the drawbacks of conventional LP-based approaches is the biasing of estimated formants by harmonic peaks. Methods such as discrete all-pole modeling and weighted LP have been proposed to overcome this problem, but they all use a linear frequency scale. This study proposes a new LP technique, frequency-warped time-weighted linear prediction (WWLP), to provide spectral envelope estimates robust to harmonic peaks that work on a warped frequency scale that approximates the sensitivities of the human auditory system. Experiments are performed within the context of vocoding in statistical parametric speech synthesis. Subjective listening test results show that WWLP-based spectral envelope modeling increases quality over previously developed methods.

关键词： Frequency warping linear prediction (LP) source-filter model speech synthesis

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BIOLOGICALLY INSPIRED SPEECH EMOTION RECOGNITION

BIOLOGICALLY INSPIRED SPEECH EMOTION RECOGNITION

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lotjidereshgi, Reza Gournay, Philippe Univ Sherbrooke Speech & Audio Res Grp Sherbrooke PQ J1K 2R1 Canada

ISBN: (纸本)9781509041176

Conventional feature-based classification methods do not apply well to automatic recognition of speech emotions, mostly because the precise set of spectral and prosodic features that is required to identify the emotional state of a speaker has not been determined yet. This paper presents a method that operates directly on the speech signal, thus avoiding the problematic step of feature extraction. Furthermore, this method combines the strengths of the classical source-filter model of human speech production with those of the recently introduced liquid state machine (LSM), a biologically-inspired spiking neural network (SNN). The source and vocal tract components of the speech signal are first separated and converted into perceptually relevant spectral representations. These representations are then processed separately by two reservoirs of neurons. The output of each reservoir is reduced in dimensionality and fed to a final classifier. This method is shown to provide very good classification performance on the Berlin Database of Emotional Speech (Emo-DB). This seems a very promising framework for solving efficiently many other problems in speech processing.

关键词： speech emotion recognition source-filter model liquid state machine reservoir computing

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Fundamental Frequency Estimation in Speech Signals With Variable Rate Particle filters

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2016年第5期24卷 890-900页

作者： Zhang, Geliang Godsill, Simon Univ Cambridge Signal Proc & Commun Lab Dept Engn Cambridge CB2 1PZ England

Fundamental frequency estimation, known as pitch estimation in speech signals is of interest both to the research community and to industry. Meanwhile, the particle filter is known to be a powerful Bayesian inference method to track dynamic parameters in nonlinear state-space models. In this paper, we propose a speech model under a time-varying source-filter speech model, and use variable rate particle filters (VRPF) to develop methods for estimation of pitch periods in speech signals. A Rao-Blackwellised variable rate particle filter (RBVRPF) is also implemented. The proposed VRPF and RBVRPF are compared with a state-of-the-art pitch estimation algorithm, the YIN algorithm. Simulation results show that more accurate estimation of pitch can be obtained by VRPF and RBVRPF even under strong background noise conditions.

关键词： variable rate particle filters pitch estimation Rao-Blackwellisation source-filter model

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Biologically inspired speech emotion recognition

Biologically inspired speech emotion recognition

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Reza Lotfidereshgi Philippe Gournay Speech and Audio Research Group Universite de Sherbrooke (Quebec) J1K 2R1 Canada

ISBN: (纸本)9781509041183

关键词： speech emotion recognition source-filter model liquid state machine reservoir computing emotion recognition Voice Signal Speech Feature extraction Emotions state machines Speech processing reservoirs Classification Methods Respiratory Inspiration Loudspeakers

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A Synthesis model With Intuitive Control Capabilities for Rolling Sounds

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2014年第8期22卷 1260-1273页

作者： Conan, Simon Derrien, Olivier Aramaki, Mitsuko Ystad, Solvi Kronland-Martinet, Richard Aix Marseille Univ Centrale Marseille CNRS LMAUPR 7051 F-13402 Marseille 20 France Univ Toulon & Var CNRS LMA UPR 7051 F-13402 Marseille 20 France

This paper presents a physically inspired source-filter model for rolling sound synthesis. The model, which is suitable for real-time implementation, is based on qualitative and quantitative observations obtained from a physics-based model described in the literature. In the first part of the paper, the physics-based model is presented, followed by a perceptual experiment, whose aim is to identify the perceptually relevant information characterizing the rolling interaction. On the basis of this experiment, we hypothesize that the particular pattern of the interaction force is responsible for the perception of a rolling object. A complete analysis-synthesis scheme of this interaction force is then provided, along with a description of the calibration of the proposed source-filter sound synthesis process. Finally, a mapping strategy for intuitive control of the proposed synthesis process (i.e. size and velocity of the rolling object and roughness of the surface) is proposed and validated by a listening test.

关键词： Audio virtual reality physics-based model rolling sounds sound analysis sound synthesis source-filter model

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