Paper Citation Record · LEDGER

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution

As of 18 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2506.02181.

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

pith.paper-citation-record.v1
2506.02181 v1

Coverage vector

measured 46 of 46 reference resolution

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measured 47 of 47 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: pith, observed 2026-08-07T11:32:17.963370Z

Reference resolution

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External citation measurements

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Outbound references

Observation 32be8668-a1d0-4b36-9bae-41ad920028b8 · outbound

This paper cites an unresolved cited work.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Unresolved cited work

Reference 1

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This paper cites Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution

Reference 2

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This paper cites Output text is encoded into BPE.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Output text is encoded into BPE

Reference 3

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This paper cites Time Fig.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Time Fig

Reference 4

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This paper cites The encoder layers use a convolution kernel size of31, an em- bedding size of512, and a linear layer hidden size of 2 048.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution The encoder layers use a convolution kernel size of31, an em- bedding size of512, and a linear layer hidden size of 2 048

Reference 5

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Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Unresolved cited work

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This paper cites This is the first in-depth analysis of saliency maps in relation to fine-grained acoustic patterns across three phoneme classes.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution This is the first in-depth analysis of saliency maps in relation to fine-grained acoustic patterns across three phoneme classes

Reference 7

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This paper cites Understanding the representa- tion and computation of multilayer perceptrons: A case study in speech recognition,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Understanding the representa- tion and computation of multilayer perceptrons: A case study in speech recognition,

Reference 8

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This paper cites Analyzing phonetic and graphemic representations in end-to-end automatic speech recog- nition,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Analyzing phonetic and graphemic representations in end-to-end automatic speech recog- nition,

Reference 9

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This paper cites Probing phoneme, language and speaker information in unsupervised speech representations,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Probing phoneme, language and speaker information in unsupervised speech representations,

Reference 10

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This paper cites Domain-Informed Probing of wav2vec 2.0 Embeddings for Pho- netic Features,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Domain-Informed Probing of wav2vec 2.0 Embeddings for Pho- netic Features,

Reference 11

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This paper cites Probing self- supervised speech models for phonetic and phonemic informa- tion: A case study in aspiration,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Probing self- supervised speech models for phonetic and phonemic informa- tion: A case study in aspiration,

Reference 12

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This paper cites Self-supervised speech representations are more phonetic than semantic,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Self-supervised speech representations are more phonetic than semantic,

Reference 13

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This paper cites Encoding of phonol- ogy in a recurrent neural model of grounded speech,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Encoding of phonol- ogy in a recurrent neural model of grounded speech,

Reference 14

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Observation cbaf82b0-f548-412b-b390-a1ef98ef0803 · outbound

This paper cites Learn- ing weakly supervised multimodal phoneme embeddings,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Learn- ing weakly supervised multimodal phoneme embeddings,

Reference 15

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This paper cites Acous- tic characteristics of American English vowels,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Acous- tic characteristics of American English vowels,

Reference 16

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This paper cites Neuron Activation Profiles for Interpreting Convolutional Speech Recognition Models,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Neuron Activation Profiles for Interpreting Convolutional Speech Recognition Models,

Reference 17

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This paper cites Interpretable Convolutional Filters with SincNet,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Interpretable Convolutional Filters with SincNet,

Reference 18

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This paper cites Introspection for convolutional automatic speech recognition,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Introspection for convolutional automatic speech recognition,

Reference 19

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This paper cites End-to-end acoustic modeling using convolutional neural networks for HMM- based automatic speech recognition,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution End-to-end acoustic modeling using convolutional neural networks for HMM- based automatic speech recognition,

Reference 20

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This paper cites Gradient-Adjusted Neuron Activation Profiles for Comprehensive Introspection of Convolutional Speech Recognition Models.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Gradient-Adjusted Neuron Activation Profiles for Comprehensive Introspection of Convolutional Speech Recognition Models

Reference 21

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This paper cites Directly Comparing the Listening Strategies of Humans and Machines,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Directly Comparing the Listening Strategies of Humans and Machines,

Reference 22

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Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution SPES: Spectrogram Perturbation for Explainable Speech-to-Text Generation

Reference 23

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This paper cites Burst and Transition Cues to V oicing Perception for Spo- ken Initial Stops by Impaired- and Normal-Hearing Listeners,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Burst and Transition Cues to V oicing Perception for Spo- ken Initial Stops by Impaired- and Normal-Hearing Listeners,

Reference 25

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Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Acoustic cues of voiced and voiceless plosives for determining place of articulation,

Reference 26

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Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Acoustic characteristics of English fricatives,

Reference 27

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Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Acoustic characteristics of clearly spoken English fricatives,

Reference 28

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Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Conformer: Convolution-augmented Transformer for Speech Recognition,

Reference 29

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This paper cites Introducing Parsel- mouth: A Python interface to Praat,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Introducing Parsel- mouth: A Python interface to Praat,

Reference 30

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Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Pykaldi: A Python Wrapper for Kaldi,

Reference 31

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Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Neural Machine Transla- tion of Rare Words with Subword Units,

Reference 32

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This paper cites SentencePiece: A simple and lan- guage independent subword tokenizer and detokenizer for Neural Text Processing,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution SentencePiece: A simple and lan- guage independent subword tokenizer and detokenizer for Neural Text Processing,

Reference 33

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Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Attention is All you Need,

Reference 34

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This paper cites Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq,

Reference 35

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Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Com- mon V oice: A Massively-Multilingual Speech Corpus,

Reference 36

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This paper cites Lib- rispeech: An ASR corpus based on public domain audio books,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Lib- rispeech: An ASR corpus based on public domain audio books,

Reference 37

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This paper cites TED-LIUM 3: Twice as Much Data and Corpus Repartition for Experiments on Speaker Adaptation,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution TED-LIUM 3: Twice as Much Data and Corpus Repartition for Experiments on Speaker Adaptation,

Reference 38

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This paper cites V oxPopuli: A Large- Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution V oxPopuli: A Large- Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation,

Reference 39

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Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Rethinking the Inception Architecture for Computer Vision,

Reference 40

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Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Con- nectionist temporal classification: labelling unsegmented se- quence data with recurrent neural networks,

Reference 41

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This paper cites Adam: A Method for Stochastic Opti- mization,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Adam: A Method for Stochastic Opti- mization,

Reference 42

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Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution SpecAugment: A Simple Data Aug- mentation Method for Automatic Speech Recognition,

Reference 43

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This paper cites Darpa timit acoustic-phonetic continuous speech corpus cd-rom TIMIT,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Darpa timit acoustic-phonetic continuous speech corpus cd-rom TIMIT,

Reference 44

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This paper cites Twists, humps, and pebbles: Multilingual speech recognition models exhibit gen- der performance gaps,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Twists, humps, and pebbles: Multilingual speech recognition models exhibit gen- der performance gaps,

Reference 45

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This paper cites Burst spectrum as a cue for the stop voicing contrast in American English,.

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution Burst spectrum as a cue for the stop voicing contrast in American English,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:18.247636Z

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Pith citing papers

Observation 0b075e5a-482f-47e0-ab8a-9b01b6920469 · inbound