Enhancement of multiband excitation (MBE)by pitch-cycle waveform coding

Yang, Haiyun; Koh, Soo Ngee; Sivaprakasapillai, P.

doi:10.1049/el:19941123

Cited by 2 publications

(3 citation statements)

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“…17. Evolution of the identification rate as a function of α and β, when the first and second eigenresiduals (µ 1 (n) and µ 2 (n)) are combined according to Equations ( 5) and (6).…”

Section: Results On the Timit Database 1) Usefulness Of The Glottal S...mentioning

confidence: 99%

“…STRAIGHT makes use of a specific spectral envelope obtained via a pitch-adaptive time-frequency smoothing of the FFT speech spectrum. As for the excitation modeling, STRAIGHT relies on aperiodic measurements in five spectral subbands: [0-1], [1][2], [2][3][4], [4][5][6] and [6][7][8] kHz. As a consequence, the excitation features used by the HMM synthesizer now include the 5 aperiodic measurements besides F 0 .…”

Section: Experiments Of Hmm-based Speech Synthesismentioning

confidence: 99%

“…In the Codebook Excited Linear Predictive (CELP) approach [4], the residual signal is constructed from a codebook containing several typical excitation frames [5]. The Multi Band Excitation (MBE) modeling [6] suggests to divide the frequency axis in several bands, and a voiced/unvoiced decision is taken for each band at each time. According to the Mixed Excitation (ME) approach [7], the residual signal is the superposition of both a periodic and a non-periodic component.…”

Section: Introductionmentioning

confidence: 99%

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The Deterministic Plus Stochastic Model of the Residual Signal and Its Applications

Drugman

Dutoit

2012

IEEE Trans. Audio Speech Lang. Process.

112

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Speech generated by parametric synthesizers generally suffers from a typical buzziness, similar to what was encountered in old LPC-like vocoders. In order to alleviate this problem, a more suited modeling of the excitation should be adopted. For this, we hereby propose an adaptation of the Deterministic plus Stochastic Model (DSM) for the residual. In this model, the excitation is divided into two distinct spectral bands delimited by the maximum voiced frequency. The deterministic part concerns the low-frequency contents and consists of a decomposition of pitch-synchronous residual frames on an orthonormal basis obtained by Principal Component Analysis. The stochastic component is a high-pass filtered noise whose time structure is modulated by an energy-envelope, similarly to what is done in the Harmonic plus Noise Model (HNM). The proposed residual model is integrated within a HMM-based speech synthesizer and is compared to the traditional excitation through a subjective test. Results show a significative improvement for both male and female voices. In addition the proposed model requires few computational load and memory, which is essential for its integration in commercial applications.

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“…17. Evolution of the identification rate as a function of α and β, when the first and second eigenresiduals (µ 1 (n) and µ 2 (n)) are combined according to Equations ( 5) and (6).…”

Section: Results On the Timit Database 1) Usefulness Of The Glottal S...mentioning

confidence: 99%