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arxiv: 2605.18001 · v1 · pith:CDEQULPQnew · submitted 2026-05-18 · 💻 cs.CL

Bridging the Gap: Converting Read Text to Conversational Dialogue

Parshav Singla , Agnik Banerjee , Aaditya Arora , Shruti Aggarwal , Anil Kumar Verma , Vikram C M , Raj Prakash Gohil , Gopal Kumar Agarwal This is my paper

Pith reviewed 2026-05-20 11:28 UTC · model grok-4.3

classification 💻 cs.CL
keywords speech conversionconversational speechprosodic featuresdeep neural networksHiFi-GANnaturalnessMean Opinion ScorePACC
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The pith

A prosodic adjustment method converts read speech to more natural conversational speech using deep neural networks and HiFi-GAN synthesis.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Prosodic Adjustment with Conversational Context (PACC) to transform read speech, which often sounds flat, into speech that better matches the flow of real conversations. It does so by employing deep neural networks to tweak features like intonation, stress, and rhythm based on conversational context. The modified speech is then synthesized using High-Fidelity Generative Adversarial Networks, or HiFi-GAN. Experiments show gains in perceived naturalness and model accuracy after extra training on speech datasets. This could help applications such as virtual assistants and language tools produce speech that feels less mechanical.

Core claim

The paper claims that its PACC approach, which uses deep neural networks to adjust prosodic features such as intonation, stress, and rhythm in read speech and then applies HiFi-GAN for synthesis, produces conversational speech with significant improvements in naturalness and better model accuracy, thereby setting new benchmarks for speech conversion tasks and Mean Opinion Score evaluations.

What carries the argument

Prosodic Adjustment with Conversational Context (PACC), a system that analyzes read speech and modifies its prosodic elements with deep neural networks before generating output audio via HiFi-GAN.

Load-bearing premise

That prosodic adjustments via deep neural networks combined with HiFi-GAN synthesis will reliably increase perceived naturalness without reducing intelligibility or introducing artifacts, and that the chosen speech datasets adequately represent conversational contexts.

What would settle it

A controlled listening test where participants rate the naturalness and intelligibility of the converted speech against unmodified read speech and a strong baseline, showing no statistically significant gains or even losses, would falsify the central improvements.

Figures

Figures reproduced from arXiv: 2605.18001 by Aaditya Arora, Agnik Banerjee, Anil Kumar Verma, Gopal Kumar Agarwal, Parshav Singla, Raj Prakash Gohil, Shruti Aggarwal, Vikram C M.

Figure 1
Figure 1. Figure 1: The architecture of HiFi-GAN The entire model is composed of a generator and two discriminators. Both discriminators can be further divided into smaller sub￾networks, that work at different resolutions. The loss functions take as inputs intermediate feature maps and outputs of those sub￾networks. After training, the generator is used for synthesis, and the discriminators are discarded. All three components… view at source ↗
Figure 3
Figure 3. Figure 3: Waveform of synthesized conversational speech. Description: The provided waveform graph in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
read the original abstract

In recent advancements within speech processing, converting read speech to conversational speech has gained significant attention. The primary challenge in this domain is maintaining naturalness and intelligibility while minimizing computational overhead for real-time applications. Traditional read speech often lacks the nuanced prosodic variation essential for natural conversational interactions, posing challenges for applications in virtual assistants, customer service, and language learning tools. This paper introduces a novel approach, Prosodic Adjustment with Conversational Context (PACC), aimed at converting read speech into natural conversational speech used in various modern applications. PACC utilizes advanced deep neural networks to analyze and modify prosodic features such as intonation, stress, and rhythm. Unlike conventional methods, our approach uses High-Fidelity Generative Adversarial Networks (HiFi-GAN) for speech synthesis. Our experimental results demonstrate significant improvements in speech conversion, enhancing naturalness and achieving better model accuracy with additional training on speech datasets. This research establishes new benchmarks in speech conversion tasks and Mean Opinion Score (MOS) evaluation for testing model accuracy, and we show that our approach can be successfully extended to other speech conversion applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces Prosodic Adjustment with Conversational Context (PACC), a method that employs deep neural networks to analyze and adjust prosodic features (intonation, stress, rhythm) of read speech, followed by HiFi-GAN waveform synthesis, with the goal of producing more natural conversational speech. It claims significant improvements in naturalness and model accuracy, plus the establishment of new benchmarks in speech conversion tasks and Mean Opinion Score (MOS) evaluation, and suggests extensibility to other applications.

Significance. If the experimental claims hold with proper controls and reporting, the work could offer a practical route to more natural prosody in speech synthesis for virtual assistants and language tools. The use of HiFi-GAN for synthesis is a standard but relevant choice; however, the absence of any quantitative results, baselines, or protocol details in the abstract leaves the significance difficult to evaluate.

major comments (2)
  1. Abstract: The central claims of 'significant improvements in speech conversion, enhancing naturalness and achieving better model accuracy' and 'establishes new benchmarks in speech conversion tasks and Mean Opinion Score (MOS) evaluation' are asserted without any reported numerical results, baselines, error bars, dataset details, or experimental protocol. This leaves the primary contribution unsupported by visible evidence.
  2. Title vs. Abstract: The title promises conversion of 'Read Text' to 'Conversational Dialogue', which would require text parsing, dialogue-act modeling, or generation of new utterances. The abstract instead describes only prosodic feature adjustment on existing read-speech waveforms followed by synthesis, with no mention of text input or dialogue generation. If the full manuscript follows the abstract, the experimental results cannot substantiate the headline claim of bridging read text to conversational dialogue.
minor comments (2)
  1. Abstract: The phrase 'with additional training on speech datasets' is vague; specify which datasets, how they differ from the evaluation set, and whether they represent conversational contexts.
  2. Abstract: 'Mean Opinion Score (MOS) evaluation for testing model accuracy' conflates subjective naturalness ratings with objective accuracy; clarify the distinction and report both if applicable.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comments point by point below and outline revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: Abstract: The central claims of 'significant improvements in speech conversion, enhancing naturalness and achieving better model accuracy' and 'establishes new benchmarks in speech conversion tasks and Mean Opinion Score (MOS) evaluation' are asserted without any reported numerical results, baselines, error bars, dataset details, or experimental protocol. This leaves the primary contribution unsupported by visible evidence.

    Authors: We agree that the abstract would benefit from explicit quantitative support. The full manuscript reports MOS scores, baseline comparisons, dataset details, and experimental protocols in the Experiments and Results sections. We will revise the abstract to include key numerical results (e.g., specific MOS improvements and dataset references) so that the claims are directly substantiated within the abstract itself. revision: yes

  2. Referee: Title vs. Abstract: The title promises conversion of 'Read Text' to 'Conversational Dialogue', which would require text parsing, dialogue-act modeling, or generation of new utterances. The abstract instead describes only prosodic feature adjustment on existing read-speech waveforms followed by synthesis, with no mention of text input or dialogue generation. If the full manuscript follows the abstract, the experimental results cannot substantiate the headline claim of bridging read text to conversational dialogue.

    Authors: We acknowledge the mismatch in scope. The work performs prosodic adjustment on read-speech waveforms (typically derived from text) to produce conversational-style speech using PACC and HiFi-GAN, without text parsing, dialogue-act modeling, or generation of new utterances. The original title was intended to evoke the application context but does not accurately describe the technical contribution. We will revise the title to 'Bridging the Gap: Converting Read Speech to Conversational Speech via Prosodic Adjustment' and update the abstract and introduction to explicitly state the method's scope and limitations. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical claims with no derivation chain present

full rationale

The paper introduces the PACC method for prosodic adjustment of read speech using deep neural networks followed by HiFi-GAN synthesis and reports experimental improvements in naturalness and MOS scores. No equations, first-principles derivations, fitted parameters renamed as predictions, or self-citation load-bearing uniqueness theorems appear in the abstract or described content. The central claims rest on empirical results rather than any chain that reduces to its own inputs by construction. The title-content mismatch noted in the skeptic analysis concerns task description accuracy, not circularity in a derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities can be extracted or audited.

pith-pipeline@v0.9.0 · 5746 in / 1139 out tokens · 29637 ms · 2026-05-20T11:28:13.485538+00:00 · methodology

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Reference graph

Works this paper leans on

28 extracted references · 28 canonical work pages

  1. [1]

    Introduction Speech synthesis and transformation technologies have seen remarkable advancements in recent years [5], driven by the growing demand for natural and engaging human -computer interactions. Virtual assistants, automated customer service systems, and *Work completed during Internship at R&D Institute, Samsung, Bangalore, India educational tools ...

    work page

  2. [2]

    This process involves intricate manipulations of linguistic and acoustic features to imbue synthesized speech with naturalness and spontaneity

    Literature Review 2.1 Understanding the Dynamics of Speech Transformation The transformation of read speech into conversational speech represents a multifaceted endeavour at the intersection of linguistics, signal processing, and artificial intelligence. This process involves intricate manipulations of linguistic and acoustic features to imbue synthesized...

    work page

  3. [3]

    Narrow Band (NB): HiFi-GAN achieves MOS scores between 3 and 5 for NB speech, indicating moderate quality perceived by listeners

    work page

  4. [4]

    Wide Band (WB): The MOS scores for WB speech range from 5 to 7, showing an improvement in perceived quality

    work page

  5. [5]

    Super Wide Band (SWB): MOS scores for SWB speech are between 6 and 8, reflecting higher perceived quality and closer to natural speech

    work page

  6. [6]

    Full Band (FB): HiFi-GAN's performance peaks with FB speech, with MOS scores consistently above 7, indicating excellent quality. These results affirm that HiFi -GAN effectively captures and reproduces the intricate features of conversational speech, providing high-fidelity outputs especially in broader bandwidth categories. 2.4.2 Conclusion on HiFi-GAN Pe...

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  7. [7]

    The primary challenge is to modify the prosody, rhythm , and intonation of read speech to make it sound more natural and conversational

    Problem Statement Synthesized speech from read text often lacks the natural texture and intonation of human conversational speech, leading to a deficient user experience in voice -based systems. The primary challenge is to modify the prosody, rhythm , and intonation of read speech to make it sound more natural and conversational. We aim to address this ga...

    work page

  8. [8]

    This involves improving the prosody, rhythm, and intonation of synthesized speech to closely mim ic human conversational patterns [10]

    Develop and Enhance Conversational Speech Synthesis : The primary objective of this research is to develop a robust model using Nvidia's HiFi -GAN to convert read speech into natural -sounding conversational speech. This involves improving the prosody, rhythm, and intonation of synthesized speech to closely mim ic human conversational patterns [10]. By le...

    work page

  9. [9]

    This comprehensive assessment will help in identifying the strengths and weaknesses of the model

    Comprehensive Evaluation and Benchmarking : Another critical objective is to rigorously evaluate the performance of the proposed model using a combination of objective metrics, such as Mel-Cepstral Distortion (MCD)Mel-Cepstral Distortion (MCD), Perceptual Evaluation of Speech Quality (PESQ), and Root Mean Squared Error (RMSE) alongside subjective evaluati...

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  10. [10]

    By focusing on these challenges, we seek to advance the current state-of-the-art in speech processing

    Addressing Key Challenges and Advancing Research : This research also aims to address the inherent challenges in transforming read speech to conversational speech, including the preservation of prosodic elements and reduction of synthesis artifacts. By focusing on these challenges, we seek to advance the current state-of-the-art in speech processing. The ...

    work page

  11. [11]

    Below is a detailed description of the dataset components and their processing:

    Datasets The dataset used in this research is meticulously curated to ensure accurate transformation of read speech into conversational speech. Below is a detailed description of the dataset components and their processing:

    work page

  12. [12]

    • Recording Specifications: Captured at a sampling rate of 16 kHz or higher, with metadata such as speaker information and textual content

    Audio Recordings of Read and Conversational Speech: • Comprises high -quality audio recordings from various sources, segmented into smaller chunks for processing. • Recording Specifications: Captured at a sampling rate of 16 kHz or higher, with metadata such as speaker information and textual content

    work page

  13. [13]

    Mel-Spectrograms: • Segmented audio recordings are transformed into Mel - spectrograms, serving as the primary input for the HiFi - GAN model. • Transformation Process : Involves short -time Fourier transform (STFT) followed by mapping frequencies to the Mel scale, retaining essential spectral features for high - quality speech synthesis

    work page

  14. [14]

    Waveform of synthesized conversational speech

    Waveform Graph: Figure 3. Waveform of synthesized conversational speech. Description: The provided waveform graph in Fig. 3 represents the amplitude variation of an audio signal over time. Content: The graph displays the dynamic nature of the speech signal, illustrating fluctuations in amplitude that are typical of natural speech patterns. Purpose: This w...

    work page

  15. [15]

    These parameters ensure a rigorous assessment of the model's performance in transforming read speech into natural, conversational speech

    Output Parameters The quality and effectiveness of the synthesized speech are evaluated using a comprehensive set of output parameters. These parameters ensure a rigorous assessment of the model's performance in transforming read speech into natural, conversational speech. The key output parameters used in this research are:

    work page

  16. [16]

    Lower MCD values indicate a closer match to the reference speech, implying higher synthesis quality

    Mel-Cepstral Distortion (MCD) • Definition: MCD measures the spectral distance between the Mel -cepstral coefficients of the reference and synthesized speech. Lower MCD values indicate a closer match to the reference speech, implying higher synthesis quality. • Application: This parameter is computed from the Mel - spectrogram data, providing an objective...

    work page

  17. [17]

    It is widely used for evaluating the quality of synthesized and transmitted speech

    Perceptual Evaluation of Speech Quality (PESQ) • Definition: PESQ is an objective measure that predicts the perceived quality of speech signals as experienced by human listeners. It is widely used for evaluating the quality of synthesized and transmitted speech. • Score Range : PESQ scores range from -0.5 to 4.5, with higher scores indicating better perce...

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  18. [18]

    It provides a straightforward measure of the differences in amplitude between the two signals

    Root Mean Squared Error (RMSE) • Definition: RMSE quantifies the average magnitude of the error between the reference and synthesized audio waveforms. It provides a straightforward measure of the differences in amplitude between the two signals. • Application: RMSE is essential for evaluating the overall accuracy of the waveform generation

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  19. [19]

    Accurate pitch representation is crucial for natural-sounding speech

    Pitch Contour Distortion (PCD) Definition: PCD measures the accuracy of the pitch contour in the synthesized speech compared to the reference. Accurate pitch representation is crucial for natural-sounding speech. • Calculation: Like RMSE, PCD is calculated over the pitch values extracted from both reference and synthesized speech

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  20. [20]

    • Procedure: Listeners evaluate the speech samples and provide scores based on their auditory perception, with higher scores indicating better quality

    Mean Opinion Score (MOS) • Definition: MOS is a subjective measure obtained by human listeners who rate the naturalness and quality of the synthesized speech on a scale from 1 to 5. • Procedure: Listeners evaluate the speech samples and provide scores based on their auditory perception, with higher scores indicating better quality. • Importance: MOS is in...

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  21. [21]

    The datasets used for evaluation include recordings of read speech, which are transformed into Mel -spectrograms for input into the HiFi -GAN model

    Experiment Results We evaluate our model for converting read speech into conversational speech using the HiFi -GAN framework against baseline models. The datasets used for evaluation include recordings of read speech, which are transformed into Mel -spectrograms for input into the HiFi -GAN model. We conducted experiments with various training parameters ...

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  22. [22]

    This is essential for ensuring that the speech sounds natural and comprehensible

    Frequency Distribution: The spectrogram shows a rich distribution of frequencies, indicating that the synthesized speech maintains the necessary spectral detail. This is essential for ensuring that the speech sounds natural and comprehensible

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  23. [23]

    The clear variations over time reflect natural speech patterns, including pauses and changes in pitch

    Temporal Dynamics: The time axis shows how the frequency content evolves, which is crucial for capturing the rhythmic and prosodic elements of speech. The clear variations over time reflect natural speech patterns, including pauses and changes in pitch

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  24. [24]

    This is important for producing expressive and natural-sounding speech

    Amplitude Variations: The variations in amplitude across different frequencies and times indicate dynamic changes in speech loudness and intensity. This is important for producing expressive and natural-sounding speech. Importance in Speech Synthesis The Mel-spectrogram is not only a diagnostic tool but also an intermediate representation used by the HiFi...

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  25. [25]

    Our method leverages the generation of Mel -spectrograms to capture intricate spectral details and utilizes HiFi -GAN for high -fidelity speech synthesis

    Conclusion This paper presents a comprehensive approach to converting read speech into conversational speech using the HiFi -GAN model developed by NVIDIA. Our method leverages the generation of Mel -spectrograms to capture intricate spectral details and utilizes HiFi -GAN for high -fidelity speech synthesis. Through extensive experiments, we demonstrated...

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  26. [26]

    Shruti Aggarwal and Dr

    Acknowledgements We would like to thank Samsung Research Team members Vikram C M , Raj Prakash Gohil , Gopal Kumar Agarwal and extend special thanks to our faculties Dr. Shruti Aggarwal and Dr. Anil Kumar Verma for their invaluable support and guidance throughout this project

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  27. [27]

    References spectrogram similarity measures further affirm the fidelity and naturalness of the outputs, highlighting the model's capability to capture subtle nuances in speech. Our findings suggest that the HiFi -GAN model, combined with Mel-spectrogram inputs, offers a robust framework for improving the quality of speech synthesis systems, particularly fo...

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  28. [28]

    By effectively transforming read speech into Mel -spectrograms, we capture intricate spectral details, which are crucial for high-fidelity speech synthesis

    Inference The application of our HiFi -GAN model demonstrates a significant improvement in the naturalness and intonation of synthesized speech, achieving a quality that closely mimics human conversational patterns. By effectively transforming read speech into Mel -spectrograms, we capture intricate spectral details, which are crucial for high-fidelity sp...

    work page 2014