This paper gives a general overview of techniques in statistical parametric speech synthesis. One of the instances of these techniques, called HMM-based generation synthesis (or simply HMM-based synthesis), has recently been shown to be very effective in generating acceptable speech synthesis. This paper also contrasts these techniques with the more conventional unit selection technology that has dominated speech synthesis over the last ten years. Advantages and disadvantages of statistical parametric synthesis are highlighted as well as identifying where we expect the key developments to appear in the immediate future.

https://www.sp.nitech.ac.jp/~bonanza/Paper/EMIME/zen_specom.pdf

Statistical Parametric Speech Synthesis

Conventional approaches to statistical parametric speech synthesis typically use decision tree-clustered context-dependent hidden Markov models (HMMs) to represent probability densities of speech parameters given texts. Speech parameters are generated from the probability densities to maximize their output probabilities, then a speech waveform is reconstructed from the generated parameters. This approach is reasonably effective but has a couple of limitations, e.g. decision trees are inefficient to model complex context dependencies. This paper examines an alternative scheme that is based on a deep neural network (DNN). The relationship between input texts and their acoustic realizations is modeled by a DNN. The use of the DNN can address some limitations of the conventional approach. Experimental results show that the DNN-based systems outperformed the HMM-based systems with similar numbers of parameters.

/pdf/statistical-parametric-speech-synthesis-using-deep-neural-17f2zgma1n.pdf

Statistical parametric speech synthesis using deep neural networks

This paper investigates data augmentation for deep neural network acoustic modeling based on label-preserving transformations to deal with data sparsity. Two data augmentation approaches, vocal tract length perturbation (VTLP) and stochastic feature mapping (SFM), are investigated for both deep neural networks (DNNs) and convolutional neural networks (CNNs). The approaches are focused on increasing speaker and speech variations of the limited training data such that the acoustic models trained with the augmented data are more robust to such variations. In addition, a two-stage data augmentation scheme based on a stacked architecture is proposed to combine VTLP and SFM as complementary approaches. Experiments are conducted on Assamese and Haitian Creole, two development languages of the IARPA Babel program, and improved performance on automatic speech recognition (ASR) and keyword search (KWS) is reported.

Data augmentation for deep neural network acoustic modeling

While biometric authentication has advanced significantly in recent years, evidence shows the technology can be susceptible to malicious spoofing attacks. The research community has resp onded with dedicated countermeasures which aim to detect and deflect such attacks. Even if the literature shows that they can be effective, the problem is far from being solved; biometric systems remain vulnerable to spoofing. Despite a growing momentum to develo p spoofing countermeasures for automatic speaker verificati on, now that the technology has matured suffi ciently to support mass deployment in an array of diverse applications, greater effort will be needed in the future to ensure adequate protection against spoofing. This article provides a survey of past work and ide ntifies priority research directions for the future. We summarise previous studies involving impersonation, replay, speech synthesis and voice conversion spoofing attacks and more recent e fforts to develop dedicated countermeasures. The survey shows that future research should address the lack of standard datasets and the over-fitting of existing countermeasures to specific, know n spoofing attacks.

http://www.cs.joensuu.fi/pages/tkinnu/webpage/pdf/spoofingsurvey2014_SPCOM.pdf

Spoofing and countermeasures for speaker verification: a sur vey

This paper proposes a novel approach to voice conversion with non-parallel training data. The idea is to bridge between speakers by means of Phonetic PosteriorGrams (PPGs) obtained from a speaker-independent automatic speech recognition (SI-ASR) system. It is assumed that these PPGs can represent articulation of speech sounds in a speaker-normalized space and correspond to spoken content speaker-independently. The proposed approach first obtains PPGs of target speech. Then, a Deep Bidirectional Long Short-Term Memory based Recurrent Neural Network (DBLSTM) structure is used to model the relationships between the PPGs and acoustic features of the target speech. To convert arbitrary source speech, we obtain its PPGs from the same SI-ASR and feed them into the trained DBLSTM for generating converted speech. Our approach has two main advantages: 1) no parallel training data is required; 2) a trained model can be applied to any other source speaker for a fixed target speaker (i.e., many-to-one conversion). Experiments show that our approach performs equally well or better than state-of-the-art systems in both speech quality and speaker similarity.

Phonetic posteriorgrams for many-to-one voice conversion without parallel data training

INTERSPEECH2008: 9th Annual Conference of the International Speech Communication Association, September 22-26, 2008, Brisbane, Australia.

Low-delay voice conversion based on maximum likelihood estimation of spectral parameter trajectory.

This paper presents an implementation of real-time processing of statistical voice conversion (VC) based on Gaussian mixture models (GMMs). To develop VC applications for enhancing our human-to-human speech communication, it is essential to implement real-time conversion processing. Moreover, it is useful to reduce computational complexity of the conversion processing for making VC applications available even in limited resources. In this paper, we propose a real-time VC method based on a low-delay conversion algorithm considering dynamic features and a global variance. Moreover, we also propose a computationally efficient VC method based on rapid source feature extraction and diagonalization of full covariance matrices. Some experimental results are presented to show that the proposed methods work reasonably well.

Takashi Muramatsu

Papers

Low-delay voice conversion based on maximum likelihood estimation of spectral parameter trajectory.

Implementation of Computationally Efficient Real-Time Voice Conversion.

Low-Delay Voice Conversion Based on Maximum Likelihood Estimation of Spectral Parameter Trajectory