Paper Citation Record · LEDGER

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments

As of 9 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2506.03554.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.03554 v1

Coverage vector

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Pith citing papers itemized under the disclosed page cap.

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A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T11:03:09.354907Z

Reference resolution

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation 2efe9a14-4863-4878-84fe-2e09512cdfbe · outbound

This paper cites One key component of this progress is neural vocoders, which synthesize audio waveforms from acoustic fea- tures.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments One key component of this progress is neural vocoders, which synthesize audio waveforms from acoustic fea- tures

Reference 1

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Observation a8644e48-5651-4605-8c30-96adb3581fc6 · outbound

This paper cites Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments

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This paper cites throughput We analyze the relationship between latency and throughput via block streaming synthesis using several neural vocoders.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments throughput We analyze the relationship between latency and throughput via block streaming synthesis using several neural vocoders

Reference 3

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This paper cites Wavehax and MS-Wavehax utilized F0 for generating prior signals, whereas other models concate- nate it with the mel-spectrogram, resulting in a 101-dimensional input feature.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Wavehax and MS-Wavehax utilized F0 for generating prior signals, whereas other models concate- nate it with the mel-spectrogram, resulting in a 101-dimensional input feature

Reference 4

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Observation 8a563839-47b6-4221-ad58-b0e62c571d16 · outbound

This paper cites Next, we evaluate its speech quality under causal and non-causal condi- tions, compared to the vocoders described in Section 3.1.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Next, we evaluate its speech quality under causal and non-causal condi- tions, compared to the vocoders described in Section 3.1

Reference 5

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Observation 494c870b-c8e6-48d0-ac85-86a610e4cf49 · outbound

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Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Unresolved cited work

Reference 6

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This paper cites Our analysis revealed that streaming throughput de- pends on overhead from data and parameter loading as well as computational complexity.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Our analysis revealed that streaming throughput de- pends on overhead from data and parameter loading as well as computational complexity

Reference 7

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Observation 740076dc-5257-4382-91a1-d16bf60bde88 · outbound

This paper cites Generative Ad- versarial Nets,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Generative Ad- versarial Nets,

Reference 9

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Observation 1fa39c74-ec56-4793-a3e2-f68e742d76e0 · outbound

This paper cites MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:09.685081Z

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This paper cites HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis,

Reference 11

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Observation f31581db-3072-41f3-9d29-a576c8f35400 · outbound

This paper cites iSTFTNet: Fast and Lightweight Mel-Spectrogram V ocoder Incorporating Inverse Short-Time Fourier Transform,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments iSTFTNet: Fast and Lightweight Mel-Spectrogram V ocoder Incorporating Inverse Short-Time Fourier Transform,

Reference 12

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Observation d080a97c-771d-4f78-a9d7-2ee0952f5583 · outbound

This paper cites iSTFTNet2: Faster and More Lightweight iSTFT-Based Neural V ocoder Using 1D- 2D CNN,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments iSTFTNet2: Faster and More Lightweight iSTFT-Based Neural V ocoder Using 1D- 2D CNN,

Reference 13

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Observation 8574965a-ee7c-4caa-b7cf-233f52f36f98 · outbound

This paper cites APNet: An All-Frame-Level Neural V ocoder Incorporating Direct Prediction of Amplitude and Phase Spectra,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments APNet: An All-Frame-Level Neural V ocoder Incorporating Direct Prediction of Amplitude and Phase Spectra,

Reference 14

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This paper cites V ocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments V ocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis,

Reference 15

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This paper cites AC-VC: Non-Parallel Low La- tency Phonetic Posteriorgrams Based V oice Conversion,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments AC-VC: Non-Parallel Low La- tency Phonetic Posteriorgrams Based V oice Conversion,

Reference 16

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Observation dcec3a08-34b7-45bf-a098-15219e497a6e · outbound

This paper cites Low-latency real-time non-parallel voice conversion based on cyclic variational autoencoder and multiband WaveRNN with data-driven linear prediction,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Low-latency real-time non-parallel voice conversion based on cyclic variational autoencoder and multiband WaveRNN with data-driven linear prediction,

Reference 17

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Observation cad249c8-ffe8-45be-a510-2acefc71d38f · outbound

This paper cites An Investigation of Streaming Non-Autoregressive sequence-to-sequence V oice Con- version,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments An Investigation of Streaming Non-Autoregressive sequence-to-sequence V oice Con- version,

Reference 18

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Observation 6ed446e1-84ab-4cac-9979-056845733a0b · outbound

This paper cites Streaming non-autoregressive model for any-to-many voice conversion.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Streaming non-autoregressive model for any-to-many voice conversion

Reference 19

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This paper cites Wavehax: Aliasing-Free Neural Waveform Synthesis Based on 2D Convo- lution and Harmonic Prior for Reliable Complex Spectrogram Es- timation,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Wavehax: Aliasing-Free Neural Waveform Synthesis Based on 2D Convo- lution and Harmonic Prior for Reliable Complex Spectrogram Es- timation,

Reference 20

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This paper cites Multi-Stream HiFi-GAN with Data-Driven Waveform Decomposition,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Multi-Stream HiFi-GAN with Data-Driven Waveform Decomposition,

Reference 21

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This paper cites Implementation of DNN-based real-time voice conversion and its improvements by audio data augmentation and mask-shaped device,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Implementation of DNN-based real-time voice conversion and its improvements by audio data augmentation and mask-shaped device,

Reference 22

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This paper cites Real-Time, Full-Band, Online DNN-Based V oice Conversion System Using a Single CPU,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Real-Time, Full-Band, Online DNN-Based V oice Conversion System Using a Single CPU,

Reference 23

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This paper cites Fregrad: Lightweight and Fast Frequency-Aware Diffusion V ocoder,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Fregrad: Lightweight and Fast Frequency-Aware Diffusion V ocoder,

Reference 24

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Observation 1e4c5a7c-83fe-45ba-8011-8ee975ddc3de · outbound

This paper cites Incremental Text-to-Speech Syn- thesis with Prefix-to-Prefix Framework,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Incremental Text-to-Speech Syn- thesis with Prefix-to-Prefix Framework,

Reference 25

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This paper cites Neural iTTS: Toward Synthesizing Speech in Real-time with End-to-end Neural Text- to-Speech Framework,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Neural iTTS: Toward Synthesizing Speech in Real-time with End-to-end Neural Text- to-Speech Framework,

Reference 26

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Observation 8cf19f83-7642-40c9-a31d-c81d3184e6f2 · outbound

This paper cites High Qual- ity Streaming Speech Synthesis with Low, Sentence-Length- Independent Latency,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments High Qual- ity Streaming Speech Synthesis with Low, Sentence-Length- Independent Latency,

Reference 27

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This paper cites A ConvNet for the 2020s,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments A ConvNet for the 2020s,

Reference 28

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This paper cites Design and evaluation of parallel quadrature mirror filters (PQMF),.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Design and evaluation of parallel quadrature mirror filters (PQMF),

Reference 29

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This paper cites Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech,

Reference 30

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This paper cites Fre-GAN: Adversar- ial Frequency-Consistent Audio Synthesis,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Fre-GAN: Adversar- ial Frequency-Consistent Audio Synthesis,

Reference 31

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This paper cites Harvest: A High-Performance Fundamental Fre- quency Estimator from Speech Signals,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Harvest: A High-Performance Fundamental Fre- quency Estimator from Speech Signals,

Reference 32

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Observation dbbbdaa0-e510-4d26-9275-092f49970bb8 · outbound

This paper cites Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assess- ment of telephone networks and codecs,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assess- ment of telephone networks and codecs,

Reference 33

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This paper cites UTMOS: UTokyo-SaruLab System for V oiceMOS Challenge 2022,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments UTMOS: UTokyo-SaruLab System for V oiceMOS Challenge 2022,

Reference 34

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This paper cites Lightweight and High-Fidelity End-to-End Text-to-Speech with Multi-Band Generation and Inverse Short-Time Fourier Transform,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Lightweight and High-Fidelity End-to-End Text-to-Speech with Multi-Band Generation and Inverse Short-Time Fourier Transform,

Reference 35

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This paper cites Aggregate.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Aggregate

Reference 36

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This paper cites Developing Real-Time Stream- ing Transformer Transducer for Speech Recognition on Large- Scale Dataset,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Developing Real-Time Stream- ing Transformer Transducer for Speech Recognition on Large- Scale Dataset,

Reference 37

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Observation de3f64f1-3a9d-4411-a7e0-03fffb896ebe · outbound

This paper cites Layer Normalization.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Layer Normalization

Reference 38

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This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:09.448743Z

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Observation cd0c716b-1b79-40bb-9899-f87c7b297c54 · outbound

This paper cites JVS corpus: free Japanese multi-speaker voice corpus.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments JVS corpus: free Japanese multi-speaker voice corpus

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:09.198316Z

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This paper cites BigVGAN: A Universal Neural V ocoder with Large-Scale Training,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments BigVGAN: A Universal Neural V ocoder with Large-Scale Training,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:09.428879Z

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Observation 5128eaf3-493a-497b-9c95-2a6f70845f3a · outbound

This paper cites UnivNet: A Neural V ocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments UnivNet: A Neural V ocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:09.418774Z

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Observation 467c237e-279d-48b1-87dd-b5a5fb64ba68 · outbound

This paper cites JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:09.213625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c145dd8-8aec-461c-b1cf-893d1d63cae1 · outbound

This paper cites Matcha-TTS: A Fast TTS Architecture with Conditional Flow Matching,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Matcha-TTS: A Fast TTS Architecture with Conditional Flow Matching,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:09.408771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 81d8bd2e-2a86-4167-b268-d0ff0e89b1a6 · outbound

This paper cites Attention is All you Need,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Attention is All you Need,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:09.400038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a7c8b92f-06ea-41dc-a53e-f2366fc7cbe3 · outbound

This paper cites Flow Matching for Generative Modeling,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Flow Matching for Generative Modeling,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:09.392214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 567c61dd-d2d7-4d1b-99f0-b6810a1a0687 · outbound

This paper cites Matcha-tts.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments Matcha-tts

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:09.382954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7d0d27f3-920b-440a-b8bc-65fd66d3f718 · outbound

This paper cites jsut-label.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments jsut-label

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:09.374466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 17222672-317a-478c-8678-94b2f5604230 · outbound

This paper cites What the Future Brings: Investigating the Impact of Lookahead for Incremental Neural TTS,.

Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments What the Future Brings: Investigating the Impact of Lookahead for Incremental Neural TTS,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:09.366129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation a8644e48-5651-4605-8c30-96adb3581fc6 · inbound