Paper Citation Record · LEDGER

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data

As of 8 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2507.00534.

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

pith.paper-citation-record.v1
2507.00534 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:19:32.183322Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:19:27.758474Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T21:19:32.623093Z

Reference resolution

55 of 55 outbound references displayed

verified exact3
verified fuzzy45
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4ab4d996-905f-44ec-b76b-515621ff2663 · outbound

This paper cites NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:19:32.685490Z

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Observation 67d13a80-55eb-4a5e-a9fe-933dc9dc9747 · outbound

This paper cites Prior work includes domain-specific ASR sub-models [19] and monolingual hybrid CTC-transformer adaptation [20], both fo- cusing on domain-incremental setups.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Prior work includes domain-specific ASR sub-models [19] and monolingual hybrid CTC-transformer adaptation [20], both fo- cusing on domain-incremental setups

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:41.644919Z

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Observation fccfe1b7-cab0-4997-a21b-26243b404fa1 · outbound

This paper cites We now introduce definitions which will be used through the paper.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data We now introduce definitions which will be used through the paper

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:41.322399Z

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Observation c62f29a0-7087-487b-8821-4ef6ffcf695e · outbound

This paper cites Continual Learning Methods Below, we list down all the approaches considered in this work.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Continual Learning Methods Below, we list down all the approaches considered in this work

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:40.590691Z

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Observation fe696370-eb8b-4319-b473-8422d17bef74 · outbound

This paper cites an unresolved cited work.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:19:40.290349Z

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Observation ee96945d-4f99-44d8-9ab6-99bec66c8db8 · outbound

This paper cites an unresolved cited work.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:19:39.999791Z

Source-reported events for the cited work

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Observation 62d1d76f-6756-49ad-a55f-975474c36ba1 · outbound

This paper cites Common voice: A massively-multilingual speech corpus,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Common voice: A massively-multilingual speech corpus,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:39.631758Z

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Observation 43309cca-58d5-4665-9aed-a02a757f4e1e · outbound

This paper cites V oxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data V oxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:39.229195Z

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Observation 89e2bc6e-4e76-4c1f-bd8e-465bf8961b07 · outbound

This paper cites Pseudo-labeling for massively multilingual speech recognition,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Pseudo-labeling for massively multilingual speech recognition,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:38.914368Z

Source-reported events for the cited work

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Observation e0bb7c10-cdcc-4571-9bf4-5f113577c3d4 · outbound

This paper cites an unresolved cited work.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:19:40.995127Z

Source-reported events for the cited work

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Observation 950e6ecf-de07-4cd5-87a2-7ae86be86f28 · outbound

This paper cites Robust speech recognition via large-scale weak supervision,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Robust speech recognition via large-scale weak supervision,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:38.674638Z

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Observation 5762d61d-65b1-4767-ac42-aa0f67418fbd · outbound

This paper cites Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T21:19:28.630761Z

Source-reported events for the cited work

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Observation 6f8d6e97-28ca-4e15-8340-242c4bbf030a · outbound

This paper cites SPRING-INX: A Multilingual Indian Language Speech Corpus by SPRING Lab, IIT Madras.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data SPRING-INX: A Multilingual Indian Language Speech Corpus by SPRING Lab, IIT Madras

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T21:19:28.711069Z

Source-reported events for the cited work

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Observation 8740fea4-66c3-4575-bbeb-ad644d52bd22 · outbound

This paper cites A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:38.249254Z

Source-reported events for the cited work

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Observation 781ef588-109e-4fb4-b2b2-e344195b6dea · outbound

This paper cites An empirical investigation of catastrophic forgeting in gradient-based neural networks,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data An empirical investigation of catastrophic forgeting in gradient-based neural networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:38.146314Z

Source-reported events for the cited work

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Observation 94e41438-2f11-4fae-af34-f5e84617e5b6 · outbound

This paper cites Continual learning through synaptic intelligence,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Continual learning through synaptic intelligence,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:37.937453Z

Source-reported events for the cited work

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Observation fb522024-c274-46b0-892e-22c5717de2fb · outbound

This paper cites The CLEAR benchmark: Continual learning on real-world imagery,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data The CLEAR benchmark: Continual learning on real-world imagery,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:37.824779Z

Source-reported events for the cited work

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Observation 7e0be13f-69aa-4c60-af97-114a9430dfb8 · outbound

This paper cites Learning multiple visual domains with residual adapters,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Learning multiple visual domains with residual adapters,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:37.713034Z

Source-reported events for the cited work

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Observation a0dc0497-ab2c-4837-8272-c6e0f3821d54 · outbound

This paper cites Learn continually, generalize rapidly: Life- long knowledge accumulation for few-shot learning,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Learn continually, generalize rapidly: Life- long knowledge accumulation for few-shot learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:37.605711Z

Source-reported events for the cited work

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Observation 69d820ce-f5a2-41bc-a1cf-f93958550c05 · outbound

This paper cites Indicvoices: Towards building an inclusive mul- tilingual speech dataset for indian languages,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Indicvoices: Towards building an inclusive mul- tilingual speech dataset for indian languages,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:37.478917Z

Source-reported events for the cited work

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Observation 4b781d15-b722-4cfd-9826-5d55c73fb200 · outbound

This paper cites Experience replay for continual learning,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Experience replay for continual learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:37.360682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88a24495-c4c9-413e-8092-a2e0e37e7b4e · outbound

This paper cites Overcoming catastrophic forgetting in graph neural networks,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Overcoming catastrophic forgetting in graph neural networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:37.142229Z

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Observation b82e7422-3f82-402f-9645-1c8383100708 · outbound

This paper cites Memory aware synapses: Learning what (not) to forget,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Memory aware synapses: Learning what (not) to forget,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:37.023315Z

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Observation 5884146a-fa72-4296-8fd4-8b3d8cbe0289 · outbound

This paper cites Three types of incremental learning,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Three types of incremental learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:36.900154Z

Source-reported events for the cited work

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Observation 39d3d29d-74f8-4c97-95ab-eb8bdac29b4b · outbound

This paper cites Continual learning in automatic speech recogni- tion,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Continual learning in automatic speech recogni- tion,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:36.797127Z

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Observation 9f16b083-12b7-4a25-a565-4fc9ef07c2f9 · outbound

This paper cites Towards lifelong learning of end-to-end ASR,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Towards lifelong learning of end-to-end ASR,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:36.686301Z

Source-reported events for the cited work

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Observation 33f7da61-d177-4233-b0a7-913ec1028ab9 · outbound

This paper cites CL-MASR: A continual learning benchmark for multilingual ASR,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data CL-MASR: A continual learning benchmark for multilingual ASR,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:36.567639Z

Source-reported events for the cited work

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Observation 2cbeb7ac-70ee-4802-9976-6adf5ecc3988 · outbound

This paper cites Core50: a new dataset and benchmark for continuous object recognition,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Core50: a new dataset and benchmark for continuous object recognition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:36.419129Z

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Observation 8269bba3-5eef-4fc9-be6b-ef4eab4484f7 · outbound

This paper cites Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:19:32.513122Z

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Observation 591c627c-b5cb-4672-92cb-9037763b76da · outbound

This paper cites A comprehensive survey of continual learn- ing: Theory, method and application,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data A comprehensive survey of continual learn- ing: Theory, method and application,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:38.470291Z

Source-reported events for the cited work

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Observation 56aa7de2-13ae-482e-b4ef-6581118bf528 · outbound

This paper cites Dark experience for general continual learning: a strong, simple baseline,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Dark experience for general continual learning: a strong, simple baseline,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:36.274116Z

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Observation 2af800ae-b816-4a9d-bd9f-3c20caaa1997 · outbound

This paper cites Efficient lifelong learning with A-GEM,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Efficient lifelong learning with A-GEM,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:36.126016Z

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Observation b9b7cd96-9e5a-4a2b-b222-c14c9e4c9189 · outbound

This paper cites Using adapters to overcome catastrophic for- getting in end-to-end automatic speech recognition,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Using adapters to overcome catastrophic for- getting in end-to-end automatic speech recognition,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:35.974028Z

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Observation ca872ceb-056b-495f-96a0-3eb0d88215af · outbound

This paper cites Progressive Neural Networks.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Progressive Neural Networks

Reference 35

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Observation b2fe595a-a55b-4d2e-8dd3-a37bd7e819de · outbound

This paper cites Packnet: Adding multiple tasks to a single net- work by iterative pruning,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Packnet: Adding multiple tasks to a single net- work by iterative pruning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:35.812617Z

Source-reported events for the cited work

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Observation 57c5379f-522b-4f07-810d-8b423424124e · outbound

This paper cites Interspeech 2018 low resource au- tomatic speech recognition challenge for indian languages,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Interspeech 2018 low resource au- tomatic speech recognition challenge for indian languages,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:35.678661Z

Source-reported events for the cited work

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Observation f1568df4-3fc6-43a9-b851-5f3c1b2ba550 · outbound

This paper cites Crowd-sourced speech corpora for ja- vanese, sundanese, sinhala, nepali, and bangladeshi bengali,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Crowd-sourced speech corpora for ja- vanese, sundanese, sinhala, nepali, and bangladeshi bengali,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:35.520778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3fca9009-1475-4866-bebb-086e2a7d8fca · outbound

This paper cites Open-source multi-speaker speech corpora for building gujarati, kannada, malayalam, marathi, tamil and telugu speech synthesis systems,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Open-source multi-speaker speech corpora for building gujarati, kannada, malayalam, marathi, tamil and telugu speech synthesis systems,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:35.391829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation eb4b5e06-65a1-468f-b7d8-2073c4095be8 · outbound

This paper cites MUCS 2021: Multilingual and code-switching ASR challenges for low resource indian languages,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data MUCS 2021: Multilingual and code-switching ASR challenges for low resource indian languages,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:35.233212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1d1e4d0e-63c7-4f46-b154-64e427b6fc83 · outbound

This paper cites Indicsuperb: A speech processing universal per- formance benchmark for indian languages,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Indicsuperb: A speech processing universal per- formance benchmark for indian languages,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:35.073730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9412ccfa-9af2-40fb-8b65-ccc84417320a · outbound

This paper cites Effectiveness of mining audio and text pairs from public data for improving ASR systems for low-resource languages,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Effectiveness of mining audio and text pairs from public data for improving ASR systems for low-resource languages,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:34.898998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7addc6e0-2ddb-4409-a1a7-65701dc32852 · outbound

This paper cites Gram vaani ASR challenge on spontaneous telephone speech recordings in regional variations of hindi,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Gram vaani ASR challenge on spontaneous telephone speech recordings in regional variations of hindi,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:34.737135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4d3c0963-ab09-4f77-a239-d368ccc2ece2 · outbound

This paper cites Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:19:32.354902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9eb8b359-f3fd-490e-a2d7-56d5c5445666 · outbound

This paper cites Automatic speech recognition in sanskrit: A new speech corpus and modelling insights,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Automatic speech recognition in sanskrit: A new speech corpus and modelling insights,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:34.559261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 372687d9-c9ff-4f40-8751-d84405dfa39e · outbound

This paper cites The IIIT-H indic speech databases,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data The IIIT-H indic speech databases,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:34.380040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cc519a06-cdbc-4436-9a5a-bb7be83a293f · outbound

This paper cites Crowdsourcing speech data for low-resource languages from low-income workers,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Crowdsourcing speech data for low-resource languages from low-income workers,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:34.215258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation debad60d-3fbc-4c8d-964f-27b5ad6f156f · outbound

This paper cites Vistaar: Diverse benchmarks and training sets for indian language ASR,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Vistaar: Diverse benchmarks and training sets for indian language ASR,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:34.049890Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 55df258a-1695-4c44-a9b4-2fbfc302ba2e · outbound

This paper cites Resources for Indian languages,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Resources for Indian languages,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:33.867450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1e948c6a-0f6b-48c1-bfef-0eda3ddba8bc · outbound

This paper cites Svarah: Evaluating english ASR systems on in- dian accents,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Svarah: Evaluating english ASR systems on in- dian accents,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:33.713799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f816947f-b576-4c83-848d-94d9a13382aa · outbound

This paper cites SPIRE-SIES: A spontaneous indian english speech corpus,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data SPIRE-SIES: A spontaneous indian english speech corpus,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:33.509484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0f00a550-0de4-493b-b82a-2b4902142ced · outbound

This paper cites LAHAJA: A Robust Multi-accent Benchmark for Evaluating Hindi ASR Systems.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data LAHAJA: A Robust Multi-accent Benchmark for Evaluating Hindi ASR Systems

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T21:19:31.740390Z

Source-reported events for the cited work

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Observation 60617892-0ee7-4d7a-bf37-4932ce859511 · outbound

This paper cites Conformer: Convolution-augmented transformer for speech recognition,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Conformer: Convolution-augmented transformer for speech recognition,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:33.305737Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 52fb142b-9ec0-493f-932f-4d59bfa2ca63 · outbound

This paper cites Stateful conformer with cache-based inference for streaming automatic speech recognition,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Stateful conformer with cache-based inference for streaming automatic speech recognition,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:33.144053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 230a1a36-aeb0-4a61-af0d-70419a9327e1 · outbound

This paper cites Uncertainty-aware balancing for multilingual and multi-domain neural machine translation training,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data Uncertainty-aware balancing for multilingual and multi-domain neural machine translation training,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:32.972372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8cd37bbf-8356-41a1-be04-82770b9f2a34 · outbound

This paper cites From WER and RIL to MER and WIL: im- proved evaluation measures for connected speech recognition,.

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data From WER and RIL to MER and WIL: im- proved evaluation measures for connected speech recognition,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:19:32.860945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 4ab4d996-905f-44ec-b76b-515621ff2663 · inbound