This paper presents a survey of over 40 papers on multilingual models for Automatic Speech Recognition (ASR), focusing on models that are built with cross-lingual transfer in mind. The majority of the world's languages do not have usable ASR systems due to the lack of large speech datasets to train these models. Cross-lingual transfer is an attractive solution, as low-resource languages can potentially benefit from higher-resource languages either through transfer learning or being jointly trained in the same multilingual model. Recent advances in Self Supervised Learning (SSL) are opening up avenues for unlabeled speech data to be used in multilingual ASR models, which can pave the way for improved performance on low-resource languages. The paper addresses questions about whether multilingual models are indeed superior in performance to monolingual models for both low-resource and high-resource languages. From the papers surveyed, it is clear that multilingual models perform better than monolingual counterparts trained with the same amount of data for a single language. Combining the data of all languages available leads to better generalization and improves performance across all languages. The paper distills key findings from research in multilingual ASR to describe factors that influence cross-lingual transfer and SSL, including the size of pre-training datasets, language similarity, and domain adaptation techniques. The authors provide best practices for building multilingual models from research across diverse languages and techniques, discuss open questions, and provide recommendations for future work. Overall, this survey highlights the potential benefits of using cross-lingual transfer and SSL techniques in building more effective ASR systems for low-resource languages. By leveraging existing resources from higher-resource languages and utilizing unlabeled data through SSL techniques, researchers can improve ASR performance across multiple languages while reducing resource requirements.
- The paper presents a survey of over 40 papers on multilingual models for Automatic Speech Recognition (ASR), focusing on models built with cross-lingual transfer in mind.
- Cross-lingual transfer is an attractive solution for low-resource languages to benefit from higher-resource languages either through transfer learning or being jointly trained in the same multilingual model.
- Recent advances in Self Supervised Learning (SSL) are opening up avenues for unlabeled speech data to be used in multilingual ASR models, which can pave the way for improved performance on low-resource languages.
- Multilingual models perform better than monolingual counterparts trained with the same amount of data for a single language.
- Combining the data of all languages available leads to better generalization and improves performance across all languages.
- The paper distills key findings from research in multilingual ASR to describe factors that influence cross-lingual transfer and SSL, including the size of pre-training datasets, language similarity, and domain adaptation techniques.
- The authors provide best practices for building multilingual models from research across diverse languages and techniques, discuss open questions, and provide recommendations for future work.
- This survey highlights the potential benefits of using cross-lingual transfer and SSL techniques in building more effective ASR systems for low-resource languages.
The paper talks about how computers can understand different languages when people talk to them. They looked at many papers and found that using more than one language can help the computer understand better. This is called "cross-lingual transfer". Sometimes, the computer can learn without being told what to do, which is called "Self Supervised Learning". Using more than one language helps the computer work better for all languages. The authors of the paper give advice on how to make these computers work even better in the future.
Definitions- Multilingual: something that involves or uses multiple languages.
- Automatic Speech Recognition (ASR): a technology that allows a machine to recognize and interpret human speech.
- Cross-lingual transfer: using knowledge from one language to improve understanding in another language.
- Self Supervised Learning (SSL): learning without being explicitly told what to do by a teacher or supervisor.
- Generalization: applying knowledge gained from one situation to other similar situations.
Exploring Multilingual Models for Automatic Speech Recognition
Automatic Speech Recognition (ASR) systems are becoming increasingly important in our daily lives, from voice-activated virtual assistants to automated customer service. However, the majority of the world's languages do not have usable ASR systems due to the lack of large speech datasets needed to train these models. To address this challenge, researchers have been exploring ways to leverage existing resources from higher-resource languages and utilize unlabeled data through Self Supervised Learning (SSL) techniques in order to improve performance across multiple languages while reducing resource requirements. In a recent survey paper, researchers explore multilingual models for ASR and discuss best practices for building such models with cross-lingual transfer in mind.
Background on Multilingual Models
The paper presents a survey of over 40 papers on multilingual models for ASR that focus on cross-lingual transfer. Cross-lingual transfer is an attractive solution as low-resource languages can potentially benefit from higher-resource languages either through transfer learning or being jointly trained in the same multilingual model. Recent advances in SSL are opening up avenues for unlabeled speech data to be used in multilingual ASR models, which can pave the way for improved performance on low-resource languages. The authors distill key findings from research in multilingual ASR and describe factors that influence cross-lingual transfer and SSL, including size of pre-training datasets, language similarity, and domain adaptation techniques.
Performance Comparison between Monolingual and Multilingual Models
The paper addresses questions about whether multilingual models are indeed superior in performance to monolingual models for both low-resource and high-resource languages. From the papers surveyed it is clear that when trained with the same amount of data per language, multilingual models perform better than monolingual counterparts across all languages tested. Combining data from all available languages leads to better generalization resulting in improved performance overall compared with using only one language’s dataset alone.
Best Practices & Recommendations
The authors provide best practices based on research across diverse languages and techniques as well as open questions related to building more effective ASR systems for low resource settings using cross linguistic transfer approaches combined with SSL methods such as unsupervised pre training or self supervised learning algorithms . They also provide recommendations for future work such as further exploration into how different types of architectures affect model performance , potential applications beyond speech recognition , utilizing additional sources of information such as text corpora , leveraging multi modal inputs , incorporating domain adaptation techniques , etc .
Conclusion
Overall this survey highlights the potential benefits of using cross linguistic transfer approaches combined with SSL methods such as unsupervised pre training or self supervised learning algorithms when building more effective ASR systems specifically designed for low resource settings . By leveraging existing resources from higher resource settings along with utilizing unlabeled data through SSL techniques , researchers can improve overall system accuracy while reducing resource requirements significantly .
