Applying the harmonic plus noise model in concatenative speech synthesis

By Yannis Stylianou. IEEE Transactions on Speech and Audio Processing, 2001

384 citations recorded by the source corpus.

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Vocaine the vocoder and applications in speech synthesis (2015, neutral): “During that period, many speech waveform models were proposed for use in voice transformation and voice conversion, e.g. the Harmonicplus-Noise Model (HNM) [5], [6].”
A Deep Learning Approach to Data-driven Parameterizations for Statistical Parametric Speech Synthesis (2014, background): “While many different parameterizations of the spectrum have been developed for synthesis[3][4][5][6], few have yet managed to survive in the long run.”
An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning (2020, background): “It is known to produce high speaker similarity and voice quality [77], [153],”
Generative Adversarial Network-Based Glottal Waveform Model for Statistical Parametric Speech Synthesis (2017, methodology): “Vocoders used in SPSS can be grouped into three main categories: mixed/impulse excited vocoders (e.g. STRAIGHT [8, 9]), glottal vocoders (e.g. Glot-tHMM [10] and GlottDNN [11]), and sinusoidal vocoders (e.g. quasi harmonic model [12, 13]).”
Quasi-Periodic Parallel WaveGAN Vocoder: A Non-autoregressive Pitch-dependent Dilated Convolution Model for Parametric Speech Generation (2020, result): “The results confirm our assumptions of the QP structure, and the behavior, which is similar to the harmonic plus noise model [33, 34], of QPPWG is more tractable and interpretable than that of vanilla PWG.”