Pseudo-Labeling for Massively Multilingual Speech Recognition
Author:
Affiliation:
1. McGill University/ Mila
2. Facebook AI Research
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09746832.pdf?arnumber=9746832
Reference34 articles.
1. CAPE: Encoding relative positions with continuous augmented positional embeddings;likhomanenko;NeurIPS,2021
2. Multilingual Speech Recognition with a Single End-to-End Model
3. SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
4. Rethinking Evaluation in ASR: Are Our Models Robust Enough?
5. RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions
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1. Partitioning Attention Weight: Mitigating Adverse Effect of Incorrect Pseudo-Labels for Self-Supervised ASR;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024
2. Boosting Cross-Domain Speech Recognition With Self-Supervision;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024
3. Multilinguality in Misinformation Detection;The Information Retrieval Series;2024
4. Optimizing Two-Pass Cross-Lingual Transfer Learning: Phoneme Recognition And Phoneme To Grapheme Translation;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16
5. Assessing Audio-Based Transformer Models for Speech Emotion Recognition;2023 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS);2023-11-23
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