Paper Citation Record · LEDGER

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results

As of 7 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2607.19049.

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

pith.paper-citation-record.v1
2607.19049 v1

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Pith citing papers itemized under the disclosed page cap.

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External citation measurements

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Outbound references

Observation 9c5686d6-688f-4aab-b6d8-1bbc94fa0106 · outbound

This paper cites Speech recognition by machines and humans,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Speech recognition by machines and humans,

Reference 1

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This paper cites The Interspeech 2008 Consonant Challenge.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results The Interspeech 2008 Consonant Challenge

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This paper cites Robustness of spectro-temporal features against intrinsic and extrinsic variations in automatic speech recogni- tion,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Robustness of spectro-temporal features against intrinsic and extrinsic variations in automatic speech recogni- tion,

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This paper cites What’s the difference? Comparing humans and machines on the aurora2 speech recognition task,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results What’s the difference? Comparing humans and machines on the aurora2 speech recognition task,

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This paper cites Comparing human and automatic speech recognition in simple and complex acoustic scenes,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Comparing human and automatic speech recognition in simple and complex acoustic scenes,

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This paper cites A comparison of automatic and human speech recognition in null grammar,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results A comparison of automatic and human speech recognition in null grammar,

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This paper cites Toward human parity in conversational speech recognition,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Toward human parity in conversational speech recognition,

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This paper cites Transcription methods for consistency, volume and efficiency,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Transcription methods for consistency, volume and efficiency,

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This paper cites Speech recognition in adverse conditions by humans and machines.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Speech recognition in adverse conditions by humans and machines

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Wav2Vec 2.0: A framework for self-supervised learning of speech representations,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Whisper-large-v3,

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This paper cites Evaluation of automatic speech recognition for conversational speech in Dutch, English and German: What goes missing?.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Evaluation of automatic speech recognition for conversational speech in Dutch, English and German: What goes missing?

Reference 12

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This paper cites Revisiting parity of human vs. machine conversational speech tran- scription,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Revisiting parity of human vs. machine conversational speech tran- scription,

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This paper cites Bidirec- tional LSTM-RNN for improving automated assessment of non-native children’s speech,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Bidirec- tional LSTM-RNN for improving automated assessment of non-native children’s speech,

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This paper cites Towards inclusive automatic speech recognition,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Towards inclusive automatic speech recognition,

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This paper cites Bias in Flemish automatic speech recognition,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Bias in Flemish automatic speech recognition,

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This paper cites End-to-end neural systems for automatic children speech recognition: An empirical study,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results End-to-end neural systems for automatic children speech recognition: An empirical study,

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This paper cites Automatic recognition of Dutch dysarthric speech, a pilot study,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Automatic recognition of Dutch dysarthric speech, a pilot study,

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This paper cites Phonetic analysis of dysarthric speech tempo and applications to robust personalised dysarthric speech recognition,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Phonetic analysis of dysarthric speech tempo and applications to robust personalised dysarthric speech recognition,

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This paper cites Recent progress in the CUHK dysarthric speech recognition system,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Recent progress in the CUHK dysarthric speech recognition system,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Cross-lingual self-supervised speech repre- sentations for improved dysarthric speech recognition,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Zero-shot recognition of dysarthric speech using commercial automatic speech recognition and multimodal large language models,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results On the impact of dysarthric speech on contemporary ASR cloud platforms,

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This paper cites Low-resource automatic speech recognition and error analyses of oral cancer speech,.

Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Low-resource automatic speech recognition and error analyses of oral cancer speech,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Evaluation of speech intelligibility for children with cleft lip and palate by means of automatic speech recognition,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results See what I’m saying? Comparing intelligent personal assistant use for native and non-native language speakers,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Do you understand the words that are comin outta my mouth? V oice assistant comprehension of medication names,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Mit- igating bias against non-native accents,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Racial disparities in automated speech recognition,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Gender and dialect bias in YouTube’s automatic captions,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Effects of talker dialect, gender & race on accuracy of Bing speech and YouTube automatic captions

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Unresolved cited work

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Speech-to-text: Transcription models,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results JASMIN- CGN: Extension of the Spoken Dutch Corpus with speech of elderly people, children and non-natives in the human-machine interaction modality,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results FFmpeg loudnorm

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Lexical information drives perceptual learning of distorted speech: evidence from the comprehension of noise-vocoded sentences

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Speech recognition performance disparities between Dutch diverse speaker groups

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Un- supervised cross-lingual representation learning for speech recognition

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results The Spoken Dutch Corpus. Overview and first evalua- tion,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Bootstrap estimates for confidence intervals in ASR performance evaluation,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Good practices for evaluation of machine learning systems

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Emmeans: Estimated marginal means, aka least-squares means

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Ageing voices: The effect of changes in voice parameters on ASR Performance,

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Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results Uncovering bias in asr systems: Evaluating Wav2vec2 and Whisper for dutch speakers,

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Pith citing papers

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