29346827
Wideband Harmonic Model: Alignment and Noise Modeling for High Quality Speech Synthesis
Speech sinusoidal modeling has been successfully applied to a broad range of speech analysis, synthesis and modification tasks. However, developing a high fidelity full band sinusoidal model that preserves its high quality on speech transformation still remains an open research problem. Such a system can be extremely useful for high quality speech synthesis. In this paper we present an enhanced harmonic model representation for voiced/mixed wide band speech that is capable of high quality speech reconstruction and transformation in the parametric domain. Two key elements of the proposed model are a proper phase alignment and a decomposition of a speech frame to "deterministic" and dense "stochastic" harmonic model representations that can be separately manipulated. The coupling of stochastic harmonic representation with the deterministic one is performed by means of intra-frame periodic energy envelope, estimated at analysis time and preserved during original/transformed speech reconstruction. In addition, we present a compact representation of the stochastic harmonic component, so that the proposed model has less parameters than the regular full band harmonic model, with better Signal to Reconstruction Error performance. On top of that, the improved phase alignment of the proposed model provides better phase coherency in transformed speech, resulting in better quality of speech transformations. We demonstrate the subjective and objective performance of the new model on speech reconstruction and pitch modification tasks. Performance of the proposed model within unit selection TTS is also presented.
11162436
High Quality Sinusoidal Modeling of Wideband Speech for the Purposes of Speech Synthesis and Modification
This paper describes an efficient sinusoidal modeling framework for high quality wide band (WB) speech synthesis and modification. This technique may serve as a basis for speech compression in the context of small footprint concatenative Text to Speech systems. In addition, it is a useful representation for voice transformation and morphing purposes, e.g., simultaneous pitch modification and spectral envelope warping. The conventional sinusoidal modeling is enhanced with an adaptive frequency dithering mechanism, based on a degree of voicing analysis. Considerable reduction of the amount of model parameters is achieved by high band phase extension. The proposed model is evaluated and compared to the alternative STRAIGHT framework [1]. Being simpler and considerably more efficient than STRAIGHT, it outperforms it in speech quality for both speech reconstruction and transformation.
10303365
Applying the harmonic plus noise model in concatenative speech synthesis
This paper describes the application of the harmonic plus noise model (HNM) for concatenative text-to-speech (TTS) synthesis. In the context of HNM, speech signals are represented as a time-varying harmonic component plus a modulated noise component. The decomposition of a speech signal into these two components allows for more natural-sounding modifications of the signal (e.g., by using different and better adapted schemes to modify each component). The parametric representation of speech using HNM provides a straightforward way of smoothing discontinuities of acoustic units around concatenation points. Formal listening tests have shown that HNM provides high-quality speech synthesis while outperforming other models for synthesis (e.g., TD-PSOLA) in intelligibility, naturalness, and pleasantness.
16550452
Sinusoidal model parameterization for HMM-based TTS system
A sinusoidal representation of speech is an alternative to the source-filter model. It is widely used in speech coding and unit-selection TTS, but is less common in statistical TTS frameworks. In this work we utilize Regularized Cepstral Coefficients (RCC) estimated in mel-frequency scale for amplitude spectrum envelope modeling within an HMM-based TTS platform. Improved subjective quality for mel-frequency RCC (MRCC) combined with the sinusoidal model based reconstruction is reported, compared to the state-of-the-art MGC-LSP parameters
16486943
Analysis and Synthesis of Speech Using an Adaptive Full-Band Harmonic Model
Voice models often use frequency limits to split the speech spectrum into two or more voiced/unvoiced frequency bands. However, from the voice production, the amplitude spectrum of the voiced source decreases smoothly without any abrupt frequency limit. Accordingly, multiband models struggle to estimate these limits and, as a consequence, artifacts can degrade the perceived quality. Using a linear frequency basis adapted to the non-stationarities of the speech signal, the Fan Chirp Transformation (FChT) have demonstrated harmonicity at frequencies higher than usually observed from the DFT which motivates a full-band modeling. The previously proposed Adaptive Quasi-Harmonic model (aQHM) offers even more flexibility than the FChT by using a non-linear frequency basis. In the current paper, exploiting the properties of aQHM, we describe a full-band Adaptive Harmonic Model (aHM) along with detailed descriptions of its corresponding algorithms for the estimation of harmonics up to the Nyquist frequency. Formal listening tests show that the speech reconstructed using aHM is nearly indistinguishable from the original speech. Experiments with synthetic signals also show that the proposed aHM globally outperforms previous sinusoidal and harmonic models in terms of precision in estimating the sinusoidal parameters. As a perspective, such a precision is interesting for building higher level models upon the sinusoidal parameters, like spectral envelopes for speech synthesis.
792623
Pitch modifications of speech based on an adaptive Harmonic Model
In this paper, a simple method for pitch-scale modifications of speech based on a recently suggested model for AM-FM decomposition of speech signals, is presented. This model is referred to as the adaptive Harmonic Model (aHM). The aHM models speech as a sum of harmonically related sinusoids that can adapt to the local characteristics of the signal. It was shown that this model provides high quality reconstruction of speech and thus, it can also provide high quality pitch-scale modifications. For the latter, the amplitude envelope is estimated using the Discrete All-Pole (DAP) method, and the phase envelope estimation is performed by utilizing the concept of relative phase. Formal listening tests on a database of several languages show that the synthetic pitch-scaled waveforms are natural and free of some common artefacts encountered in other state-of-the-art models, such as HNM and STRAIGHT.
11807447
Transient modeling for overlap-add sinusoidal model of speech
Speech sinusoidal modeling has been successfully applied to a broad range of speech analysis, synthesis and modification tasks. At most, it reproduces a high quality speech, however for speech transients (e.g. plosives, glottal stops) it suffers from reduced fidelity due to lack of intra-frame modeling of irregularities. Various extensions had been proposed for the stationary sinusoidal model to cope with this problem. One of simple and well-known in the art approaches is incorporating of an intra-frame magnitude envelope into the sinusoidal model. It used to be done by iterative analysis-by-synthesis procedure. In this paper we derive an optimal analytic solution for this problem. We will show that this solution yields significantly better model fit than the known-in-the-art analysis-by-synthesis approach.
18872335
Quantization of the spectral envelope for sinusoidal coders
In an effort to efficiently code the spectral envelope of speech signals for wideband speech coding based on sinusoidal models, a robust computation of the discrete cepstrum coefficients and their quantization is investigated. A parameterization of the spectral envelope has been proposed which is based on discrete cepstral coefficients using regularization techniques. This paper presents an efficient quantization scheme for these coefficients in order to use them in applications like speech coding. We present results which show a 35% reduction in the bit rate when compared to simple scalar quantization. To verify the efficiency of the proposed quantization schemes, informal listening tests were performed in the context of a sinusoidal coder.
61523446
Speech coding: a tutorial review
The past decade has witnessed substantial progress towards the application of low-rate speech coders to civilian and military communications as well as computer-related voice applications. Central to this progress has been the development of new speech coders capable of producing high-quality speech at low data rates. Most of these coders incorporate mechanisms to: represent the spectral properties of speech, provide for speech waveform matching, and "optimize" the coder's performance for the human ear. A number of these coders have already been adopted in national and international cellular telephony standards. The objective of this paper is to provide a tutorial overview of speech coding methodologies with emphasis on those algorithms that are part of the recent low-rate standards for cellular communications. Although the emphasis is on the new low-rate coders, we attempt to provide a comprehensive survey by covering some of the traditional methodologies as well. We feel that this approach will not only point out key references but will also provide valuable background to the beginner. The paper starts with a historical perspective and continues with a brief discussion on the speech properties and performance measures. We then proceed with descriptions of waveform coders, sinusoidal transform coders, linear predictive vocoders, and analysis-by-synthesis linear predictive coders. Finally, we present concluding remarks followed by a discussion of opportunities for future research. >
16550452
Sinusoidal model parameterization for HMM-based TTS system
A sinusoidal representation of speech is an alternative to the source-filter model. It is widely used in speech coding and unit-selection TTS, but is less common in statistical TTS frameworks. In this work we utilize Regularized Cepstral Coefficients (RCC) estimated in mel-frequency scale for amplitude spectrum envelope modeling within an HMM-based TTS platform. Improved subjective quality for mel-frequency RCC (MRCC) combined with the sinusoidal model based reconstruction is reported, compared to the state-of-the-art MGC-LSP parameters
746430
COVAREP — A collaborative voice analysis repository for speech technologies
Speech processing algorithms are often developed demonstrating improvements over the state-of-the-art, but sometimes at the cost of high complexity. This makes algorithm reimplementations based on literature difficult, and thus reliable comparisons between published results and current work are hard to achieve. This paper presents a new collaborative and freely available repository for speech processing algorithms called COVAREP, which aims at fast and easy access to new speech processing algorithms and thus facilitating research in the field. We envisage that COVAREP will allow more reproducible research by strengthening complex implementations through shared contributions and openly available code which can be discussed, commented on and corrected by the community. Presently COVAREP contains contributions from five distinct laboratories and we encourage contributions from across the speech processing research field. In this paper, we provide an overview of the current offerings of COVAREP and also include a demonstration of the algorithms through an emotion classification experiment.
1872405
CROWDMOS: An approach for crowdsourcing mean opinion score studies
MOS (mean opinion score) subjective quality studies are used to evaluate many signal processing methods. Since laboratory quality studies are time consuming and expensive, researchers often run small studies with less statistical significance or use objective measures which only approximate human perception. We propose a cost-effective and convenient measure called crowdMOS, obtained by having internet users participate in a MOS-like listening study. Workers listen and rate sentences at their leisure, using their own hardware, in an environment of their choice. Since these individuals cannot be supervised, we propose methods for detecting and discarding inaccurate scores. To automate crowdMOS testing, we offer a set of freely distributable, open-source tools for Amazon Mechanical Turk, a platform designed to facilitate crowdsourcing. These tools implement the MOS testing methodology described in this paper, providing researchers with a user-friendly means of performing subjective quality evaluations without the overhead associated with laboratory studies. Finally, we demonstrate the use of crowdMOS using data from the Blizzard text-to-speech competition, showing that it delivers accurate and repeatable results.
18151124
Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency-based F0 extraction: Possible role of a repetitive structure in sounds
Abstract A set of simple new procedures has been developed to enable the real-time manipulation of speech parameters. The proposed method uses pitch-adaptive spectral analysis combined with a surface reconstruction method in the time–frequency region. The method also consists of a fundamental frequency (F0) extraction using instantaneous frequency calculation based on a new concept called `fundamentalness'. The proposed procedures preserve the details of time–frequency surfaces while almost perfectly removing fine structures due to signal periodicity. This close-to-perfect elimination of interferences and smooth F0 trajectory allow for over 600% manipulation of such speech parameters as pitch, vocal tract length, and speaking rate, while maintaining high reproductive quality.
41789919
Using deep bidirectional recurrent neural networks for prosodic-target prediction in a unit-selection text-to-speech system
Deeply-stacked Bidirectional Recurrent Neural Networks (BiRNNs) are able to capture complex, shortand long-term, context dependencies between predictors and targets due to the non-linear dependency they introduce on the entire observation when predicting a target, thanks to the use of recurrent hidden layers that accumulate information from all preceding and future observations. This aspect of the model makes them desirable for tasks such as the prediction of prosodic contours for text-to-speech systems, where the surface prosody can be a result of the interaction between local and non-local features. Although previous work has demonstrated that they attain stateof-the-art performance for this task within a parametric synthesis framework, their use within unit-selection synthesis systems remains unexplored. In this work we deploy this class of models within a unit selection system, investigate their effect on the outcome of the unit search, and perceptually evaluate it against the baseline (decision-tree-based) approach.