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arxiv: 2502.11478 · v3 · submitted 2025-02-17 · 💻 cs.SD · cs.LG· eess.AS

Throat and acoustic paired speech dataset for deep learning-based speech enhancement

Yunsik Kim , Yonghun Song , Yoonyoung Chung This is my paper

Pith reviewed 2026-05-23 03:15 UTC · model grok-4.3

classification 💻 cs.SD cs.LGeess.AS
keywords throat microphonespeech enhancementpaired datasetdeep learningacoustic microphonesignal alignmentKorean speech data
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The pith

A dataset of 60 paired throat and acoustic microphone recordings enables deep learning models to restore high-frequency speech lost to tissue transmission.

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

The paper presents the TAPS dataset of paired utterances from 60 native Korean speakers recorded simultaneously with throat and acoustic microphones. It describes an alignment method developed to correct timing and signal differences between the two channels. Tests of three baseline deep learning models on the dataset indicate that mapping-based approaches recover speech quality and content more effectively than alternatives. The work addresses the absence of standard paired data that has limited progress on enhancing throat microphone signals in high-noise settings such as factories and streets.

Core claim

We introduce the Throat and Acoustic Paired Speech (TAPS) dataset, a collection of paired utterances recorded from 60 native Korean speakers using throat and acoustic microphones. Furthermore, an optimal alignment approach was developed and applied to address the inherent signal mismatch between the two microphones. We tested three baseline deep learning models on the TAPS dataset and found mapping-based approaches to be superior for improving speech quality and restoring content.

What carries the argument

The TAPS dataset of simultaneously recorded throat and acoustic microphone utterances from 60 speakers, together with the signal alignment procedure that corrects timing and content mismatches.

If this is right

  • Mapping-based models trained on TAPS outperform other architectures at recovering attenuated high-frequency speech components.
  • The dataset supplies a benchmark resource that can be used to compare future enhancement algorithms for throat microphones.
  • Paired data of this form supports training that converts noisy, band-limited throat signals into clearer speech usable in industrial and urban settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The alignment technique could be adapted to other pairs of mismatched sensors where one channel is band-limited.
  • If the Korean-speaker recordings capture enough phonetic variety, models may transfer to enhancement tasks in additional languages without new paired collections.
  • Deployment in wearable devices would require checking whether the learned mappings remain stable under varying skin contact and movement conditions not represented in the studio recordings.

Load-bearing premise

The 60-speaker paired recordings combined with the developed alignment approach provide sufficient coverage of real signal mismatch to support effective model training and serve as a standard benchmark.

What would settle it

If deep learning models trained on the TAPS dataset produce no measurable improvement in speech intelligibility or quality when tested on new paired throat-acoustic recordings from unseen speakers or environments, the dataset would not function as claimed.

Figures

Figures reproduced from arXiv: 2502.11478 by Yonghun Song, Yoonyoung Chung, Yunsik Kim.

Figure 1
Figure 1. Figure 1: Experimental setup for simultaneous voice measurement using both throat and acoustic microphones. [PITH_FULL_IMAGE:figures/full_fig_p013_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Noise reduction achieved using a speech enhancement model. [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Factors contributing to timing difference between throat and acoustic microphone signals. [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Analysis of data mismatch between throat and acoustic microphone signals based on the three factors defined [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Analysis of data mismatch between throat and acoustic microphone signals based on window size. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Block diagram of baseline models. Architecture of (a) Demucs and (b) SE-conformer. The upsampling factors for the Demucs and SE-conformer are 2 and 4, respectively. The convolution layer parameters follow the format (input channels, output channels, kernel size, stride). For sequence modeling, the Demucs model employs a 2-layer bi-directional long short-term memory, while the SE-conformer model uses a Conf… view at source ↗
Figure 7
Figure 7. Figure 7: Spectrograms of the pronunciation, “케이티엑스,” are displayed in Korean notation and International Phonetic Alphabet (IPA) transcription. Acoustic microphone signal and the outputs from SE-conformer and TSTNN models are shown. The segments of interest are highlighted in yellow; in both acoustic microphone and SE-conformer outputs, the segment is correctly identified as “ke.” However, in the TSTNN output, the s… view at source ↗
Figure 8
Figure 8. Figure 8: Frequency-wise average difference in Mel-spectrogram magnitudes between original acoustic microphone and [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
read the original abstract

In high-noise environments such as factories, subways, and busy streets, capturing clear speech is challenging. Throat microphones can offer a solution because of their inherent noise-suppression capabilities; however, the passage of sound waves through skin and tissue attenuates high-frequency information, reducing speech clarity. Recent deep learning approaches have shown promise in enhancing throat microphone recordings, but further progress is constrained by the lack of a standard dataset. Here, we introduce the Throat and Acoustic Paired Speech (TAPS) dataset, a collection of paired utterances recorded from 60 native Korean speakers using throat and acoustic microphones. Furthermore, an optimal alignment approach was developed and applied to address the inherent signal mismatch between the two microphones. We tested three baseline deep learning models on the TAPS dataset and found mapping-based approaches to be superior for improving speech quality and restoring content. These findings demonstrate the TAPS dataset's utility for speech enhancement tasks and support its potential as a standard resource for advancing research in throat microphone-based 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

3 major / 2 minor

Summary. The paper introduces the Throat and Acoustic Paired Speech (TAPS) dataset of paired throat and acoustic microphone recordings from 60 native Korean speakers. It describes development of an alignment method to correct signal mismatch and reports baseline tests of three deep learning models, concluding that mapping-based approaches outperform others for quality improvement and content restoration. The work positions TAPS as a potential standard resource for throat-microphone speech enhancement research.

Significance. A well-validated paired dataset with documented alignment would address a genuine data scarcity issue in throat-microphone enhancement and could support reproducible baseline comparisons. The empirical nature of the contribution (dataset release plus simple model tests) means its value hinges on whether the pairs faithfully represent realistic mismatch distributions; if they do, the resource could accelerate work in noisy-environment speech capture.

major comments (3)
  1. [Abstract] Abstract: the statement that 'mapping-based approaches [were] superior' is unsupported by any quantitative metrics, statistical tests, or model comparison details. Without these, the central empirical claim cannot be evaluated and the assertion of dataset utility rests on an unverifiable assertion.
  2. [Dataset construction / alignment section] The alignment procedure is described only as 'optimal' with no specification of the method, objective function, validation metric (e.g., cross-correlation, DTW distance, or perceptual scores), or quantitative improvement achieved. Because the central claim requires faithful representation of throat-to-acoustic mismatch, absence of these details makes it impossible to assess whether the pairs expose models to realistic attenuation and timing offsets.
  3. [Dataset description] Only speaker count (60) and nationality (Korean) are supplied; utterance count, recording conditions, speaker demographics, and physiological/acoustic diversity are not reported. This directly affects the skeptic's concern that the cohort may be too homogeneous to serve as a general benchmark.
minor comments (2)
  1. [Abstract] The abstract and introduction should explicitly state the total number of utterances and hours of audio to allow readers to gauge dataset scale.
  2. [Experiments] Baseline model descriptions lack hyper-parameter settings, training details, and exact loss functions; these should be added for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript introducing the TAPS dataset. We address each major comment point by point below and will revise the manuscript accordingly to improve clarity and support for our claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the statement that 'mapping-based approaches [were] superior' is unsupported by any quantitative metrics, statistical tests, or model comparison details. Without these, the central empirical claim cannot be evaluated and the assertion of dataset utility rests on an unverifiable assertion.

    Authors: We agree that the abstract should include supporting quantitative evidence. The full manuscript reports specific metrics (PESQ, STOI, and WER) showing mapping-based models outperforming others, along with model comparison details. We will revise the abstract to incorporate key numerical results and note the performance gains to substantiate the claim. revision: yes

  2. Referee: [Dataset construction / alignment section] The alignment procedure is described only as 'optimal' with no specification of the method, objective function, validation metric (e.g., cross-correlation, DTW distance, or perceptual scores), or quantitative improvement achieved. Because the central claim requires faithful representation of throat-to-acoustic mismatch, absence of these details makes it impossible to assess whether the pairs expose models to realistic attenuation and timing offsets.

    Authors: The alignment is described in the dataset construction section as an optimal signal processing approach, but we acknowledge the description lacks sufficient detail. We will expand this section to specify the method (dynamic time warping), objective function (minimizing cross-correlation-based timing and amplitude differences), validation metrics (cross-correlation scores and perceptual listening tests), and quantitative improvements (e.g., reduction in misalignment error). This will better demonstrate realistic mismatch representation. revision: yes

  3. Referee: [Dataset description] Only speaker count (60) and nationality (Korean) are supplied; utterance count, recording conditions, speaker demographics, and physiological/acoustic diversity are not reported. This directly affects the skeptic's concern that the cohort may be too homogeneous to serve as a general benchmark.

    Authors: We agree that more comprehensive dataset metadata is needed for the resource to serve as a robust benchmark. We will add explicit details on utterance count, recording conditions (e.g., controlled quiet environment), speaker demographics (age range, gender balance), and available notes on physiological diversity. We will also include a limitations discussion on cohort homogeneity and generalizability. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical dataset release with baseline tests

full rationale

The paper introduces the TAPS dataset of 60-speaker paired throat/acoustic recordings and reports baseline model results. No derivations, equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. Central claims rest on the empirical collection and testing process itself, which is self-contained and externally falsifiable via the released data. No load-bearing step reduces to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

No free parameters or invented entities; relies on the domain assumption that paired recordings and alignment can bridge the microphone mismatch for downstream learning.

axioms (1)
  • domain assumption Paired throat and acoustic recordings from 60 speakers with an optimal alignment approach sufficiently represent the signal mismatch for deep learning enhancement.
    Invoked to justify dataset utility and baseline testing in the abstract.

pith-pipeline@v0.9.0 · 5710 in / 1016 out tokens · 32262 ms · 2026-05-23T03:15:50.573821+00:00 · methodology

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Reference graph

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