Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling | Paper Citation Record

Observation 60454f41-090e-425c-a649-77ad97fddd4f · outbound

This paper cites English conversational telephone speech recognition by humans and machines,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling English conversational telephone speech recognition by humans and machines,

Reference 1

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Observation 4bd8464f-e0e1-45ce-bb96-231cd7264821 · outbound

This paper cites Advances in joint CTC- attention based end-to-end speech recognition with a deep CNN encoder and RNN-LM,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Advances in joint CTC- attention based end-to-end speech recognition with a deep CNN encoder and RNN-LM,

Reference 2

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Observation 43229997-a51e-463f-9818-64cc8dfd18cc · outbound

This paper cites Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,

Reference 3

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Observation 02e621d9-2cd4-42e2-8d6e-d19de3729050 · outbound

This paper cites Multi- language neural network language models.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Multi- language neural network language models

Reference 4

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Observation 1d56cfaa-b6e0-41aa-b489-116f1a083a81 · outbound

This paper cites Composite em- bedding systems for zerospeech2017 track 1,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Composite em- bedding systems for zerospeech2017 track 1,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:50.633178Z

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Observation 46da4dae-9119-4834-9e29-6c5dabf1f0f9 · outbound

This paper cites The zero resource speech challenge 2017,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling The zero resource speech challenge 2017,

Reference 6

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Observation e4958f91-ca03-42e0-8f67-e018b04444c7 · outbound

This paper cites Towards unsupervised speech processing,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Towards unsupervised speech processing,

Reference 7

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Observation 37009dd0-05cc-484c-9308-3427c22754d1 · outbound

This paper cites Fully unsupervised small- vocabulary speech recognition using a segmental bayesian model,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Fully unsupervised small- vocabulary speech recognition using a segmental bayesian model,

Reference 8

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Observation d37c9f40-6d82-4b0f-a924-1cd3aa325db4 · outbound

This paper cites The zero resource speech challenge 2015.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling The zero resource speech challenge 2015

Reference 9

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Observation 2206a41a-08a2-4c23-aad8-ce3d3330f6eb · outbound

This paper cites Unsupervised bottleneck features for low-resource query-by-example spoken term detection,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Unsupervised bottleneck features for low-resource query-by-example spoken term detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:50.453146Z

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Observation c2849ebb-c96e-4ca7-a184-c0a8b5547bcd · outbound

This paper cites Pairwise learning using multi-lingual bottleneck features for low-resource query- by-example spoken term detection,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Pairwise learning using multi-lingual bottleneck features for low-resource query- by-example spoken term detection,

Reference 11

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Observation 244f66cd-beaa-4a6d-acfd-a92ccf5f75e5 · outbound

This paper cites Multilingual bottle- neck feature learning from untranscribed speech,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Multilingual bottle- neck feature learning from untranscribed speech,

Reference 12

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Observation 28a69fcb-ba50-4824-83da-5d2f04864f78 · outbound

This paper cites Deep learning methods for unsupervised acoustic modeling - LEAP submission to zerospeech challenge 2017,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Deep learning methods for unsupervised acoustic modeling - LEAP submission to zerospeech challenge 2017,

Reference 13

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Observation e64599de-9d98-432e-9f67-41ee5775d184 · outbound

This paper cites Exploiting speaker and phonetic diversity of mismatched language resources for unsupervised subword modeling,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Exploiting speaker and phonetic diversity of mismatched language resources for unsupervised subword modeling,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:50.291497Z

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Observation bfc8f3f6-5aa9-455f-9fee-e2a79fefd291 · outbound

This paper cites Weakly Supervised Multi-Embeddings Learning of Acoustic Models.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Weakly Supervised Multi-Embeddings Learning of Acoustic Models

Reference 15

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Observation 6414fa87-9e68-4a71-8c77-ec19064c8721 · outbound

This paper cites Unsupervised neu- ral network based feature extraction using weak top-down constraints,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Unsupervised neu- ral network based feature extraction using weak top-down constraints,

Reference 16

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Observation 9275d0f5-1af1-4457-a4d4-1609692ece24 · outbound

This paper cites Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages

Reference 17

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Observation c54d43f8-306c-4b34-b980-cf366934daa9 · outbound

This paper cites Parallel sampling of DP mixture models using sub-cluster splits,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Parallel sampling of DP mixture models using sub-cluster splits,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:50.207708Z

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Observation 67524ff6-cf0b-41ed-b681-f0340428fedb · outbound

This paper cites Parallel inference of Dirichlet process Gaussian mixture models for unsupervised acoustic modeling: A feasibility study,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Parallel inference of Dirichlet process Gaussian mixture models for unsupervised acoustic modeling: A feasibility study,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:50.168491Z

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Observation 0e520354-c476-40c8-8440-1117efebca78 · outbound

This paper cites Feature optimized DPGMM clus- tering for unsupervised subword modeling: A contribution to zerospeech 2017,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Feature optimized DPGMM clus- tering for unsupervised subword modeling: A contribution to zerospeech 2017,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:50.101750Z

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Observation 6d28dd58-e3e9-4007-bee3-4dae51ba17e6 · outbound

This paper cites Unsupervised speech unit discovery using k-means and neural networks,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Unsupervised speech unit discovery using k-means and neural networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:50.057008Z

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Observation 3dafe4a0-e13b-4775-84f1-67858f2a4d2e · outbound

This paper cites Iterative training of a DPGMM- HMM acoustic unit recognizer in a zero resource scenario,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Iterative training of a DPGMM- HMM acoustic unit recognizer in a zero resource scenario,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:50.018400Z

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Observation 9d700954-c8e6-47f1-9567-8a982f210ad7 · outbound

This paper cites Dirichlet process mixture of mixtures model for unsupervised subword modeling,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Dirichlet process mixture of mixtures model for unsupervised subword modeling,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.966204Z

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Observation e4566ac2-10d5-4276-8d67-748a15dbe3d2 · outbound

This paper cites Optimizing DPGMM clustering in zero-resource setting based on functional load,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Optimizing DPGMM clustering in zero-resource setting based on functional load,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.934355Z

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Observation 81a942fe-24b7-4b87-9985-311b0077a275 · outbound

This paper cites Multitask learning,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Multitask learning,

Reference 25

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This paper cites Supervised learning of acoustic models in a zero resource setting to improve DPGMM clustering,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Supervised learning of acoustic models in a zero resource setting to improve DPGMM clustering,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.874572Z

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Observation be8cee7e-e053-4ca6-96d4-ec7737926881 · outbound

This paper cites A comparison of neural network methods for unsupervised representation learning on the zero resource speech challenge,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling A comparison of neural network methods for unsupervised representation learning on the zero resource speech challenge,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.844901Z

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Observation 553e01f0-9429-4114-9638-3dc954b0afdc · outbound

This paper cites Unsupervised speech representation learning using WaveNet autoencoders.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Unsupervised speech representation learning using WaveNet autoencoders

Reference 28

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Observation 9af17fc6-06d3-4210-aafa-dd6b5a5721c8 · outbound

This paper cites Extracting bottleneck features and word-like pairs from untranscribed speech for feature representations,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Extracting bottleneck features and word-like pairs from untranscribed speech for feature representations,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.805340Z

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This paper cites Multitask feature learning for low-resource query-by-example spoken term detection,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Multitask feature learning for low-resource query-by-example spoken term detection,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.770204Z

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Observation 39fd7743-a238-4044-a729-7294f7c8a335 · outbound

This paper cites Speaker invariant feature extraction for zero-resource languages with adversarial learning,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Speaker invariant feature extraction for zero-resource languages with adversarial learning,

Reference 31

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Observation ae305bf1-8995-4a23-8299-9a84ffe4249a · outbound

This paper cites Unsuper- vised HMM posteriograms for language independent acoustic modeling in zero resource conditions,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Unsuper- vised HMM posteriograms for language independent acoustic modeling in zero resource conditions,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.674650Z

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Observation 27497a6a-02d6-4678-87db-ec17ef1da139 · outbound

This paper cites Unsupervised optimal phoneme segmentation: Objectives, algorithm and comparisons,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Unsupervised optimal phoneme segmentation: Objectives, algorithm and comparisons,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.627045Z

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Observation 90e77e77-3be3-4e32-ad6d-ce1c6b36416c · outbound

This paper cites Exploiting language-mismatched phoneme recognizers for unsupervised acoustic modeling,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Exploiting language-mismatched phoneme recognizers for unsupervised acoustic modeling,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.582126Z

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Observation e42ebaeb-7a2d-4f24-b93b-d2f8857d2852 · outbound

This paper cites Unsupervised pattern discovery from thematic speech archives based on multilingual bottleneck features,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Unsupervised pattern discovery from thematic speech archives based on multilingual bottleneck features,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.542495Z

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Observation 8b478dbf-cb35-4f2b-a021-a20167ab3e70 · outbound

This paper cites A segment model based approach to speech recognition,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling A segment model based approach to speech recognition,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.504907Z

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Observation 01db069f-b743-4add-85aa-c4186d6a81ad · outbound

This paper cites A segmental speech model with applications to word spotting,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling A segmental speech model with applications to word spotting,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.460878Z

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Observation c16d779e-4495-449e-87f8-9023ad773f0b · outbound

This paper cites Acoustic segment modeling with spectral clustering methods,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Acoustic segment modeling with spectral clustering methods,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.416917Z

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Observation 00d6d7de-d26e-4782-8c01-7dd100151ec1 · outbound

This paper cites Unsupervised speech signal to symbol transformation for zero resource speech applications,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Unsupervised speech signal to symbol transformation for zero resource speech applications,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.379285Z

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Observation d713f791-ceb0-471b-a421-4c8b2236be36 · outbound

This paper cites Unsupervised word segmenta- tion and lexicon discovery using acoustic word embeddings,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Unsupervised word segmenta- tion and lexicon discovery using acoustic word embeddings,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.318084Z

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Observation ff93fa8f-822c-4ccc-a1d7-7e45a412312c · outbound

This paper cites Economie des changements phonétiques,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Economie des changements phonétiques,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.277072Z

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Observation 6a4dcbd5-79b2-4c33-beae-1917320e975e · outbound

This paper cites an unresolved cited work.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Unresolved cited work

Reference 42

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Observation 2c9d04e5-e523-4e3e-82ca-918660c8a5b8 · outbound

This paper cites Unsupervised linear discrimi- nant analysis for supporting DPGMM clustering in the zero resource scenario,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Unsupervised linear discrimi- nant analysis for supporting DPGMM clustering in the zero resource scenario,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.199120Z

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Observation 18cac076-6b78-432c-bc41-d92af7d324ce · outbound

This paper cites Investigation into bottle-neck features for meeting speech recognition,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Investigation into bottle-neck features for meeting speech recognition,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.161754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7b76183a-7197-40f4-a85c-10e67c8ccdb8 · outbound

This paper cites Long short-term memory recurrent neural network architectures for large scale acoustic modeling.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Long short-term memory recurrent neural network architectures for large scale acoustic modeling

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.119035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 56eeaa0c-4bee-4536-a754-50a3577a8e91 · outbound

This paper cites Hybrid speech recognition with deep bidirectional LSTM,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Hybrid speech recognition with deep bidirectional LSTM,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.073129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 57b66d74-0aa2-4b09-86e8-5000b2fcc75f · outbound

This paper cites Unsupervised cross-lingual knowledge transfer in DNN-based LVCSR,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Unsupervised cross-lingual knowledge transfer in DNN-based LVCSR,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:49.037579Z

Source-reported events for the cited work

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Observation c2296650-d9f1-4638-9871-260348b4be37 · outbound

This paper cites A deep scattering spectrum-deep siamese network pipeline for unsupervised acoustic modeling,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling A deep scattering spectrum-deep siamese network pipeline for unsupervised acoustic modeling,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:48.997746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1e58dae3-5d6a-4345-9a51-6b5725ab714e · outbound

This paper cites Spoken language resources for Cantonese speech processing,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Spoken language resources for Cantonese speech processing,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:48.956323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation efeda8f3-240e-4a76-b0af-c71f815f5d00 · outbound

This paper cites The Kaldi speech recognition toolkit,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling The Kaldi speech recognition toolkit,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:48.912278Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4fef218e-5c69-44ee-b7ac-7c7eeb472a12 · outbound

This paper cites SRILM – an extensible language modeling toolkit,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling SRILM – an extensible language modeling toolkit,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:48.875894Z

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Observation 730adb89-f116-4558-9e59-cbecb485b02d · outbound

This paper cites Phoneme recognition based on long temporal context,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling Phoneme recognition based on long temporal context,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:48.829386Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 88cb5a0c-36d0-4e7d-ac9f-8aef35cbec1a · outbound

This paper cites SpeechDat- E: Five eastern european speech databases for voice-operated teleser- vices completed,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling SpeechDat- E: Five eastern european speech databases for voice-operated teleser- vices completed,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:48.777392Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2524e276-058a-48e6-a2aa-333ea20eac69 · outbound

This paper cites An efﬁcient gradient-based algorithm for on-line training of recurrent network trajectories,.

Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling An efﬁcient gradient-based algorithm for on-line training of recurrent network trajectories,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:14:48.725796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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