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Update PUBLICATIONS.md #5963

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4 changes: 4 additions & 0 deletions .gitignore
Original file line number Diff line number Diff line change
Expand Up @@ -94,6 +94,10 @@ target/
# Jupyter Notebook
.ipynb_checkpoints

# Override Jupyter in Github Language states for more accurate estimate of repo code.
# Reference: https://github.com/github/linguist/blob/master/docs/overrides.md#generated-code
*.ipynb linguist-generated
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# IPython
profile_default/
ipython_config.py
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63 changes: 61 additions & 2 deletions PUBLICATIONS.md
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Expand Up @@ -6,6 +6,21 @@ Here, we list a collection of research articles that utilize the NeMo Toolkit. I

# Automatic Speech Recognition (ASR)

<details>
<summary>2023</summary>

* [Fast Entropy-Based Methods of Word-Level Confidence Estimation for End-to-End Automatic Speech Recognition](https://ieeexplore.ieee.org/abstract/document/10022960)
* [Damage Control During Domain Adaptation for Transducer Based Automatic Speech Recognition](https://ieeexplore.ieee.org/abstract/document/10023219)

</details>

<details>
<summary>2022</summary>

* [Multi-blank Transducers for Speech Recognition](https://arxiv.org/abs/2211.03541)

</details>

<details>
<summary>2021</summary>

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## Speaker Recognition (SpkR)

<details>
<summary>2021</summary>
<summary>2022</summary>

* [TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context]( https://arxiv.org/pdf/2110.04410.pdf)
* [TitaNet: Neural Model for Speaker Representation with 1D Depth-Wise Separable Convolutions and Global Context](https://ieeexplore.ieee.org/abstract/document/9746806)

</details>

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## Speech Classification

<details>
<summary>2022</summary>

* [AmberNet: A Compact End-to-End Model for Spoken Language Identification](https://arxiv.org/abs/2210.15781)
* [Accidental Learners: Spoken Language Identification in Multilingual Self-Supervised Models](https://arxiv.org/abs/2211.05103)


</details>

<details>
<summary>2021</summary>

Expand All @@ -78,12 +102,32 @@ Here, we list a collection of research articles that utilize the NeMo Toolkit. I
</details>


--------

## Speech Translation

<details>
<summary>2022</summary>

* [NVIDIA NeMo Offline Speech Translation Systems for IWSLT 2022](https://aclanthology.org/2022.iwslt-1.18/)

</details>


--------

# Natural Language Processing (NLP)

## Language Modeling

<details>
<summary>2022</summary>

* [Evaluating Parameter Efficient Learning for Generation](https://arxiv.org/abs/2210.13673)
* [Text Mining Drug/Chemical-Protein Interactions using an Ensemble of BERT and T5 Based Models](https://arxiv.org/abs/2111.15617)

</details>

<details>
<summary>2021</summary>

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## Neural Machine Translation

<details>
<summary>2022</summary>

* [Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation](https://arxiv.org/abs/2206.01137)

</details>

<details>
<summary>2021</summary>

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# Text To Speech (TTS)

<details>
<summary>2022</summary>

* [Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers](https://arxiv.org/abs/2211.00585)

</details>

<details>
<summary>2021</summary>

Expand All @@ -140,6 +198,7 @@ Here, we list a collection of research articles that utilize the NeMo Toolkit. I
<summary>2022</summary>

* [Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text Normalization](https://arxiv.org/abs/2203.15917)
* [Thutmose Tagger: Single-pass neural model for Inverse Text Normalization](https://arxiv.org/abs/2208.00064)

</details>

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