Paper Citation Record · LEDGER

Improving Performance of End-to-End ASR on Numeric Sequences

As of 14 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:1907.01372.

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

pith.paper-citation-record.v1
1907.01372 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T11:28:56.883342Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-25T11:28:56.883342Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-25T11:30:42.391147Z

Reference resolution

31 of 31 outbound references displayed

verified exact8
verified fuzzy17
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ca4be333-30f9-4dba-8c59-df59ed981379 · outbound

This paper cites Improving Performance of End-to-End ASR on Numeric Sequences.

Improving Performance of End-to-End ASR on Numeric Sequences Improving Performance of End-to-End ASR on Numeric Sequences

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-05-25T11:30:42.393356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 22b10fe8-e240-4767-99a9-b06978b3e34f · outbound

This paper cites tagger” RNN to run on the input sequence before the sequence-to-sequence model. The tagger tags each word in the input sequence as either “trivial.

Improving Performance of End-to-End ASR on Numeric Sequences tagger” RNN to run on the input sequence before the sequence-to-sequence model. The tagger tags each word in the input sequence as either “trivial

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.157520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5b2b2faf-4858-461d-82e6-594b8baeb865 · outbound

This paper cites 22110” might be verbalized as “double two double one oh.

Improving Performance of End-to-End ASR on Numeric Sequences 22110” might be verbalized as “double two double one oh

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.191565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3b64ecb4-f2ac-4dc2-bab3-b10ee4ffca27 · outbound

This paper cites $180.50 into inr.

Improving Performance of End-to-End ASR on Numeric Sequences $180.50 into inr

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.181585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2a593872-7c79-4f1a-ac6c-f45c1b291f62 · outbound

This paper cites an unresolved cited work.

Improving Performance of End-to-End ASR on Numeric Sequences Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-05-25T11:30:43.188291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e423626b-aee2-4e10-9836-2d1c4a2a73b6 · outbound

This paper cites an unresolved cited work.

Improving Performance of End-to-End ASR on Numeric Sequences Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-05-25T11:30:43.184789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a73173fa-6219-4572-a38a-90075ce648ee · outbound

This paper cites Formatting time-aligned ASR transcripts for readability.

Improving Performance of End-to-End ASR on Numeric Sequences Formatting time-aligned ASR transcripts for readability

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-25T11:30:42.389053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c13f9f71-bc6e-49fd-a4e4-d07807eb5e42 · outbound

This paper cites Language model verbalization for automatic speech recognition.

Improving Performance of End-to-End ASR on Numeric Sequences Language model verbalization for automatic speech recognition

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.172581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f509ec84-e8f3-452e-9a68-3351d47401cb · outbound

This paper cites Query Language Modeling for V oice Search.

Improving Performance of End-to-End ASR on Numeric Sequences Query Language Modeling for V oice Search

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.175798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2aa68d51-a6e4-48f2-bb4a-e418fff84df7 · outbound

This paper cites Sequence-based class tag- ging for robust transcription in asr.

Improving Performance of End-to-End ASR on Numeric Sequences Sequence-based class tag- ging for robust transcription in asr

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.168929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cce0cf1b-620d-4f9e-96ab-3de1aca8dd4c · outbound

This paper cites Neural models of text normalization for speech applications.

Improving Performance of End-to-End ASR on Numeric Sequences Neural models of text normalization for speech applications

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.165013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3512995b-1f32-41ff-9c64-255ac733ae21 · outbound

This paper cites Streaming End-to-end Speech Recognition For Mobile Devices.

Improving Performance of End-to-End ASR on Numeric Sequences Streaming End-to-end Speech Recognition For Mobile Devices

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.178813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3910f8ab-3b77-41c5-be8e-aabb81b8009c · outbound

This paper cites State-of-the-art speech recognition with sequence- to-sequence models.

Improving Performance of End-to-End ASR on Numeric Sequences State-of-the-art speech recognition with sequence- to-sequence models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.222493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1f7cf046-c0cc-4f50-b9d7-e8633a696e1f · outbound

This paper cites Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition.

Improving Performance of End-to-End ASR on Numeric Sequences Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:30:42.374747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f4fdb771-eb85-473c-8865-f042d8e0c039 · outbound

This paper cites Synthetic Data for Text Localisation in Natural Images.

Improving Performance of End-to-End ASR on Numeric Sequences Synthetic Data for Text Localisation in Natural Images

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:30:42.348521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 155960db-4180-4e24-a09c-c14e507bd082 · outbound

This paper cites Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization.

Improving Performance of End-to-End ASR on Numeric Sequences Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:30:42.357308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6be0bfdd-7ee6-4215-872c-fdb8e8b7698a · outbound

This paper cites Streaming End-to-end Speech Recognition For Mobile Devices.

Improving Performance of End-to-End ASR on Numeric Sequences Streaming End-to-end Speech Recognition For Mobile Devices

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:30:42.364571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 383e8195-592b-4fd1-9b1e-39d70aff3eef · outbound

This paper cites A neural probabilistic language model.

Improving Performance of End-to-End ASR on Numeric Sequences A neural probabilistic language model

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-25T11:30:42.338063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eccfb3d2-45d2-4e44-9b29-f69074a2dabf · outbound

This paper cites Lstm neural networks for language modeling.

Improving Performance of End-to-End ASR on Numeric Sequences Lstm neural networks for language modeling

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.208086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fab92e8c-127a-48d8-90f7-cd3f90cb03a6 · outbound

This paper cites Recurrent neural network based language modeling in meeting recognition.

Improving Performance of End-to-End ASR on Numeric Sequences Recurrent neural network based language modeling in meeting recognition

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.194852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5c5998d9-7df4-4f1b-b2e5-d0738ba49e6f · outbound

This paper cites Multi-domain recurrent neural network language model for medical speech recognition.

Improving Performance of End-to-End ASR on Numeric Sequences Multi-domain recurrent neural network language model for medical speech recognition

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.204524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 53098557-f636-49e4-8caa-99b9e59f0a42 · outbound

This paper cites A Spelling Correction Model for End-to-End Speech Recognition.

Improving Performance of End-to-End ASR on Numeric Sequences A Spelling Correction Model for End-to-End Speech Recognition

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.215543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 77c6f3f1-64a3-454e-8b37-3a92e9384134 · outbound

This paper cites Neural error corrective language models for automatic speech recognition.

Improving Performance of End-to-End ASR on Numeric Sequences Neural error corrective language models for automatic speech recognition

Reference 23

Resolution: verified exact; doi, observed 2026-05-25T11:30:42.037758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b7a8a514-dd48-4814-a676-e8a9816b6f94 · outbound

This paper cites RNN Approaches to Text Normalization: A Challenge.

Improving Performance of End-to-End ASR on Numeric Sequences RNN Approaches to Text Normalization: A Challenge

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:30:42.342925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dc863e7e-5860-4b94-87b2-c6eacc1ae1b8 · outbound

This paper cites Generated of large-scale simulated utterances in virtual rooms to train deep-neural networks for far- ﬁeld speech recognition in Google Home.

Improving Performance of End-to-End ASR on Numeric Sequences Generated of large-scale simulated utterances in virtual rooms to train deep-neural networks for far- ﬁeld speech recognition in Google Home

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.219334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a67f33da-f524-43f0-b5d7-124030c57e42 · outbound

This paper cites Hierarchical generative modeling for controllable speech synthesis.

Improving Performance of End-to-End ASR on Numeric Sequences Hierarchical generative modeling for controllable speech synthesis

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.211922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e68f2b60-ba9a-4075-974f-fe2ec97b6a29 · outbound

This paper cites Tacotron: Towards End-to-End Speech Synthesis.

Improving Performance of End-to-End ASR on Numeric Sequences Tacotron: Towards End-to-End Speech Synthesis

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:30:42.369721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d99cf495-e56c-4ee8-901e-f2f762c7df57 · outbound

This paper cites Efficient Neural Audio Synthesis.

Improving Performance of End-to-End ASR on Numeric Sequences Efficient Neural Audio Synthesis

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:30:42.380162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e22ce3cb-8d55-4f31-9e08-c16184106b1a · outbound

This paper cites Parallel WaveNet: Fast High-Fidelity Speech Synthesis.

Improving Performance of End-to-End ASR on Numeric Sequences Parallel WaveNet: Fast High-Fidelity Speech Synthesis

Reference 30

Resolution: metadata mismatch; local_arxiv, observed 2026-05-25T11:30:42.384393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d03a2702-1e42-4346-9b77-13bc380cfd48 · outbound

This paper cites Recent advances in google real-time hmm-driven unit selection synthesizer.

Improving Performance of End-to-End ASR on Numeric Sequences Recent advances in google real-time hmm-driven unit selection synthesizer

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.198087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c1c559e8-7dce-4914-a558-3259ed205b76 · outbound

This paper cites Tensorﬂow: Large-scale machine learn- ing on heterogeneous distributed systems.

Improving Performance of End-to-End ASR on Numeric Sequences Tensorﬂow: Large-scale machine learn- ing on heterogeneous distributed systems

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:30:43.201086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation ca4be333-30f9-4dba-8c59-df59ed981379 · inbound