Litcius/Paper detail

Harmonic-Net: Fundamental Frequency and Speech Rate Controllable Fast Neural Vocoder

Keisuke Matsubara, Takuma Okamoto, Ryoichi Takashima, Tetsuya Takiguchi, Tomoki Toda, Hisashi Kawai

2023IEEE/ACM Transactions on Audio Speech and Language Processing11 citationsDOIOpen Access PDF

Abstract

There is a need to improve the synthesis quality of HiFi-GAN-based real-time neural speech waveform generative models on CPUs while preserving the controllability of fundamental frequency ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> ) and speech rate (SR). For this purpose, we propose Harmonic-Net and Harmonic-Net+, which introduce two extended functions into the HiFi-GAN generator. The first extension is a downsampling network, named the excitation signal network, that hierarchically receives multi-channel excitation signals corresponding to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> . The second extension is the layerwise pitch-dependent dilated convolutional network (LW-PDCNN), which can flexibly change its receptive fields depending on the input <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> to handle large fluctuations in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> for the upsampling-based HiFi-GAN generator. The proposed explicit input of excitation signals and LW-PDCNNs corresponding to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> are expected to realize high-quality synthesis for the normal and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> -conversion conditions and for the SR-conversion condition. The results of experiments for unseen speaker synthesis, full-band singing voice synthesis, and text-to-speech synthesis show that the proposed method with harmonic waves corresponding to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> can achieve higher synthesis quality than conventional methods in all (i.e., normal, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> -conversion, and SR-conversion) conditions.

Topics & Concepts

UpsamplingNotationAlgorithmGenerator (circuit theory)Computer scienceMathematicsTopology (electrical circuits)Speech recognitionDiscrete mathematicsAlgebra over a fieldPure mathematicsArtificial intelligenceCombinatoricsArithmeticPhysicsImage (mathematics)Power (physics)Quantum mechanicsSpeech Recognition and SynthesisSpeech and Audio ProcessingMusic and Audio Processing