Pre-training on high-resource speech recognition improves low-resource speech-to-text translation (N19-1)
Copied to clipboard
| Challenge: | Pre-training on high-resource automatic speech recognition (ASR) tasks improves ST performance even when source language is low-resourced. |
| Approach: | They propose a method to improve direct speech-to-text translation when source language is low-resource . they pre-train model on high-res automatic speech recognition task and fine-tune parameters for ST . |
| Outcome: | The proposed approach improves Spanish English ST even when the source language is low-resource . the pre-trained encoder accounts for most of the improvement, the authors show . |
Similar Papers
Unveiling the Role of Pretraining in Direct Speech Translation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to train direct speech-to-text translation systems are pretraining the encoder on automatic speech recognition, thus losing efficiency in the training process. |
| Approach: | They propose to change the decoder cross-attention to integrate source information from earlier steps in training. |
| Outcome: | The proposed model can achieve comparable performance to the pretrained model while reducing training time. |
Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation (2023.acl-long)
Copied to clipboard
| Challenge: | Using self-training or text-to-speech (TTS) to improve low-resource ASR performance is costly and can lead to catastrophic forgetting. |
| Approach: | They examine whether data augmentation techniques could help improve low-resource ASR performance . they use self-training to generate transcriptions, which are combined with original data to train new system . |
| Outcome: | The proposed approach yields a 20.5% reduction in WER compared to a system trained on 24 minutes of manually transcribed speech. |
Pretraining Language Models Using Translationese (2024.emnlp-main)
Copied to clipboard
| Challenge: | a recent study shows that large language models perform well in low-resource languages . a vast majority of languages don't have comparable data as compared to English . |
| Approach: | They propose to use Translationese as synthetic data for pre-training language models for low-resource languages. |
| Outcome: | The proposed method reduces performance of LMs trained on clean data in Indian languages . the proposed model performs better in English than in other languages, but is not comparable to English. |
Curriculum Pre-training for End-to-End Speech Translation (2020.acl-main)
Copied to clipboard
| Challenge: | End-to-end speech translation requires a powerful encoder to transcribe, understand and learn cross-lingual semantics simultaneously. |
| Approach: | They propose a curriculum pre-training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages. |
| Outcome: | The proposed method improves on En-De and En-Fr speech translation benchmarks. |
An (unhelpful) guide to selecting the best ASR architecture for your under-resourced language (2023.acl-short)
Copied to clipboard
| Challenge: | English ASR now has word error rates comparable to that of human transcriptionists, but only for the handful of the world's 7000 languages with abundant training resources. |
| Approach: | They propose to use four of the most popular ASR toolkits to train ASR models for eleven languages with limited ASR training resources: eleven widely spoken languages of Africa, Asia, and South America, one endangered language of Central America, and three critically endangered languages of North America. |
| Outcome: | The proposed architecture outperforms four of the most popular ASR toolkits for eleven languages with limited training resources. |
Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition (2024.lrec-main)
Copied to clipboard
Yash Jain, David M. Chan, Pranav Dheram, Aparna Khare, Olabanji Shonibare, Venkatesh Ravichandran, Shalini Ghosh
| Challenge: | Existing methods for pre-training for automatic speech recognition (ASR) focus on single-stage pre-train followed by fine-tuning on downstream task. |
| Approach: | They propose a multi-modal pre-training method that combines unsupervised pre-training with translation-based supervised mid-training. |
| Outcome: | The proposed method improves WERs by 38.45% over baselines on both Librispeech and SUPERB. |
A Survey of Multilingual Models for Automatic Speech Recognition (2022.lrec-1)
Copied to clipboard
| Challenge: | Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, but the majority of the world’s languages do not have usable systems due to the lack of large speech datasets to train these models. |
| Approach: | They propose to use unlabeled speech data to build multilingual ASR models that can be used for improved performance on low-resource languages. |
| Outcome: | The proposed models can be used to improve performance on low-resource languages by using unlabeled speech data. |
Pre-training via Leveraging Assisting Languages for Neural Machine Translation (2020.acl-srw)
Copied to clipboard
| Challenge: | Sequence-to-sequence (S2S) pre-training with large monolingual data is not always available for the languages of interest (LOI). |
| Approach: | They propose to use monolingual corpora of other languages to complement the scarce monolingual LOI by script mapping (Chinese to Japanese) . Using only Chinese and French monolinguals, they improve Japanese-English translation quality by up to 8.5 BLEU in low-resource scenarios. |
| Outcome: | The proposed approach improves Japanese-English translation quality by up to 8.5 BLEU in low-resource scenarios. |
Improving Low-Resource Languages in Pre-Trained Multilingual Language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained multilingual language models are the foundation of many NLP approaches, but are often not well-supported by these models due to small available monolingual corpora. |
| Approach: | They propose an unsupervised approach to improve cross-lingual representations of low-resource languages by bootstrapping word translation pairs from monolingual corpora and using them to improve language alignment. |
| Outcome: | The proposed approach improves cross-lingual representations on low-resource languages using word retrieval and zero-shot named entity recognition. |
Disentangling Pretrained Representation to Leverage Low-Resource Languages in Multilingual Machine Translation (2024.lrec-main)
Copied to clipboard
| Challenge: | Multilingual neural machine translation requires an enormous dataset, leaving the low-resource language (LRL) underdeveloped. |
| Approach: | They evaluated five languages using a parallel corpus of 1,000 instances each and found a zero-shot improvement of 7.4 from the baseline score of 7.1 to a score of 15.5 at best. |
| Outcome: | The proposed model improves performance in the linguistically diverse country of Indonesia by 7.4 from baseline score of 7.1 to 15.5 at best. |