Simple representation of signal phase for harmonic speech models

Saratxaga, Ibon; Hernáez, Inmaculada; Erro, Daniel; Navas, Eva; Sánchez, Jon

doi:10.1049/el.2009.3328

Cited by 37 publications

(22 citation statements)

References 4 publications

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“…Speech processing techniques often define c i as glottal closure instants [48,49], or as energy local maxima of a residual signal [50,51], or as pitch pulse onsets [12,14,27] for centering windows and to synchronize instantaneous phase parameters. Even though such a definition is necessary for many approaches, we will show below that it is not necessary when using the Relative Phase Shift (RPS) [24,33] or PD, which avoids an extra estimation procedure and its potential misestimation errors. In unvoiced segments, one can assume that this shape is basically random for each frame.…”

Section: Theoretical Model Of the Instantaneous Phasementioning

confidence: 99%

“…However, while amplitude envelopes are relatively easy to obtain through interpolation between sinusoidal amplitudes [22,23], the representation of phase remains an open problem. Recent attempts of obtaining a consistent phase envelope [24][25][26][27] provide features which are theoretically valid in voiced time-frequency regions but are not informative in unvoiced ones. Thus, standard speech parametrization systems used in statistical frameworks tend to discard the phase information.…”

Section: Introductionmentioning

confidence: 99%

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A uniform phase representation for the harmonic model in speech synthesis applications

Degottex

Erro

2014

J AUDIO SPEECH MUSIC PROC.

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Feature-based vocoders, e.g., STRAIGHT, offer a way to manipulate the perceived characteristics of the speech signal in speech transformation and synthesis. For the harmonic model, which provide excellent perceived quality, features for the amplitude parameters already exist (e.g., Line Spectral Frequencies (LSF), Mel-Frequency Cepstral Coefficients (MFCC)). However, because of the wrapping of the phase parameters, phase features are more difficult to design. To randomize the phase of the harmonic model during synthesis, a voicing feature is commonly used, which distinguishes voiced and unvoiced segments. However, voice production allows smooth transitions between voiced/unvoiced states which makes voicing segmentation sometimes tricky to estimate. In this article, two-phase features are suggested to represent the phase of the harmonic model in a uniform way, without voicing decision. The synthesis quality of the resulting vocoder has been evaluated, using subjective listening tests, in the context of resynthesis, pitch scaling, and Hidden Markov Model (HMM)-based synthesis. The experiments show that the suggested signal model is comparable to STRAIGHT or even better in some scenarios. They also reveal some limitations of the harmonic framework itself in the case of high fundamental frequencies.

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Section: Theoretical Model Of the Instantaneous Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A uniform phase representation for the harmonic model in speech synthesis applications

Degottex

Erro

2014

J AUDIO SPEECH MUSIC PROC.

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“…The representation was derived in (Saratxaga et al, 2009), but a brief description is provided in this section.…”

Section: Relative Phase Shift (Rps)mentioning

confidence: 99%

“…Relative Phase Shift (RPS) representation (Saratxaga et al, 2009) for the harmonic phase has also be used to build SSD systems aimed to detect spoofing signals created with adapted synthetic voices (De Leon et al, 2011) (De Leon et al, 2012 with good results. The initial works were focused on evaluating the actual capability of the RPSs to detect the phase modifications due to the synthetic generation of the spoofing signals.…”

Section: Introductionmentioning

confidence: 99%

Synthetic speech detection using phase information

Saratxaga

Sánchez

et al. 2016

Speech Communication

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Taking advantage of the fact that most of the speech processing techniques neglect the phase information, we seek to detect phase perturbations in order to prevent synthetic impostors attacking Speaker Verification systems. Two Synthetic Speech Detection (SSD) systems that use spectral phase related information are reviewed and evaluated in this work: one based on the Modified Group Delay (MGD), and the other based on the Relative Phase Shift, (RPS). A classical modulebased MFCC system is also used as baseline. Different training strategies are proposed and evaluated using both real spoofing samples and copy-synthesized signals from the natural ones, aiming to alleviate the issue of getting real data to train the systems. The recently published ASVSpoof2015 database is used for training and evaluation. Performance with completely unrelated data is also checked using synthetic speech from the Blizzard Challenge as evaluation material. The results prove that phase information can be successfully used for the SSD task even with unknown attacks.

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“…There have been various attempts at phase representation, e.g. relative phase shift [7], group delay [8], phase dispersion [9], phase distortion [10] and the complex cepstrum [6] for speech synthesis. For example, in [6] and [11], complex cepstra or a cepstrum-like representation calculated from the standard deviation of phase distortion have been modelled, respectively, using an additional independent stream in HMM-based statistical parametric speech synthesis (SPSS) to improve the quality of the vocoded speech.…”

Section: Introductionmentioning

confidence: 99%

Initial investigation of speech synthesis based on complex-valued neural networks

Yamagishi

Richmond

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Although frequency analysis often leads us to a speech signal in the complex domain, the acoustic models we frequently use are designed for real-valued data. Phase is usually ignored or modelled separately from spectral amplitude. Here, we propose a complex-valued neural network (CVNN) for directly modelling the results of the frequency analysis in the complex domain (such as the complex amplitude). We also introduce a phase encoding technique to map real-valued data (e.g. cepstra or log amplitudes) into the complex domain so we can use the same CVNN processing seamlessly. In this paper, a fully complex-valued neural network, namely a neural network where all of the weight matrices, activation functions and learning algorithms are in the complex domain, is applied for speech synthesis. Results show its ability to model both complex-valued and real-valued data.

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Simple representation of signal phase for harmonic speech models

Cited by 37 publications

References 4 publications

A uniform phase representation for the harmonic model in speech synthesis applications

A uniform phase representation for the harmonic model in speech synthesis applications

Synthetic speech detection using phase information

Initial investigation of speech synthesis based on complex-valued neural networks

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