ViSpeR: Multilingual Audio-Visual Speech Recognition

Sanath Narayan, Yasser Abdelaziz Dahou Djilali, Ankit Singh, Eustache Le Bihan, Hakim Hacid·May 27, 2024

Summary

This study introduces ViSpeR, a multilingual audio-visual speech recognition model trained on large datasets for Chinese, Spanish, Arabic, and French. The authors address data scarcity by creating a diverse and efficient data collection pipeline, combining private and public sources, and filtering videos through a binary classifier. ViSpeR outperforms previous non-English datasets, with a focus on TED talks and a Wild subset for diverse content. Experiments show better performance on Latin languages due to accent diversity and higher-quality transcriptions. The dataset, with over 3.2 million clips and 3600 hours of content, also addresses self-supervised learning and has applications in lip syncing and speaker identification. The work highlights the gap between audio-visual speech recognition and its limitations, while raising ethical considerations about biases in YouTube videos. Other research efforts in speech recognition and visual analysis leverage weak supervision and deep learning for low-resource languages.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of Audio-Visual Speech Recognition (AVSR) by presenting an extensive study on AVSR for five widely spoken languages: Chinese, Spanish, English, Arabic, and French . This research work focuses on training supervised learning models in a multi-lingual setting to achieve competitive performance on newly established benchmarks for each language . While AVSR is an increasingly important area of research, the paper contributes by providing large-scale datasets and models to serve as a foundation for further exploration in AVSR . The problem of AVSR is not entirely new, but the paper's approach and the datasets it introduces contribute significantly to advancing research in this field .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to Audio-Visual Speech Recognition (AVSR) for five widely spoken languages: Chinese, Spanish, English, Arabic, and French. The study focuses on collecting large-scale datasets for each language and training supervised learning models in a multi-lingual setting to achieve competitive performance on newly established benchmarks for each language . The research explores the challenges in training deep learning models for Visual Speech Recognition (VSR) due to the ambiguous nature of input data, lack of large-scale datasets, and complexities in acquiring VSR data compared to Audio Speech Recognition (ASR) .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "ViSpeR: Multilingual Audio-Visual Speech Recognition" proposes several new ideas, methods, and models in the field of Audio-Visual Speech Recognition (AVSR) . Here are some key points from the paper:

  1. ViSpeR Dataset: The paper introduces the ViSpeR dataset, a large-scale multilingual dataset for Audio Visual Speech Recognition. This dataset contains nearly 3.2 million clips with over 3600 hours of total duration, covering four languages: Chinese, Arabic, Spanish, and French. The clips were carefully filtered from various settings like interviews and talks to ensure diversity in the dataset .

  2. Self-Supervised Learning for VSR: The paper suggests future directions for training multilingual self-supervised models on the ViSpeR dataset to create foundational models for VSR. This involves finding suitable clustering methods to create pseudo-labels considering the multi-lingual aspect .

  3. Multi-lingual Supervised Models: The paper discusses the challenges and questions that arise when training a 'single-model-for-multiple-languages'. It raises questions about the optimal vocabulary size of the tokenizer, avoiding token switching when mixing languages, and predicting the spoken language when not known beforehand .

  4. VSR Translation: The paper explores leveraging the ViSpeR dataset to train models for translating visual speech from one language to another, such as from French to English. This capability has the potential to facilitate seamless communication across linguistic barriers .

  5. Other Applications: Apart from VSR, the ViSpeR dataset can also be used for lip syncing, speaker identification, and other applications in the field of audio-visual speech recognition .

  6. Model Training and Baselines: The paper discusses training supervised VSR and AVSR models on the ViSpeR dataset to establish them as baselines on the introduced benchmarks. The models are trained in a multi-lingual setting, resulting in competitive performance on newly established benchmarks for each language .

In summary, the paper introduces the ViSpeR dataset, proposes future research directions for self-supervised learning in VSR, discusses challenges in training multi-lingual models, explores VSR translation capabilities, and highlights various applications of the dataset beyond VSR, contributing significantly to the field of Audio-Visual Speech Recognition . The paper "ViSpeR: Multilingual Audio-Visual Speech Recognition" introduces several characteristics and advantages compared to previous methods in the field of Audio-Visual Speech Recognition (AVSR) .

  1. ViSpeR Dataset Characteristics:

    • Scale and Coverage: The ViSpeR dataset surpasses others in scale and coverage, exhibiting substantial increases in the number of clips and total duration across all languages. This makes it a comprehensive resource for non-English VSR research .
    • Diversity: The dataset contains nearly 3.2 million clips with over 3600 hours of total duration, covering four languages: Chinese, Arabic, Spanish, and French. The clips were filtered from various settings like interviews and talks to ensure diversity .
    • Test Set Quality: To ensure fair and robust evaluations, the paper takes additional measures by obtaining a second transcription using the Seamless-M4T model for a pool of considered samples to build high-quality test sets. Clips are retained based on transcripts generated by Whisper, ensuring reliability .
  2. Advantages Compared to Previous Methods:

    • Self-Supervised Learning: The paper suggests future directions for training multilingual self-supervised models on the ViSpeR dataset to create foundational models for VSR. This approach aims to find suitable clustering methods for creating pseudo-labels to account for the multi-lingual aspect, offering a more robust training method .
    • Multi-lingual Supervised Models: The paper addresses important questions when training a 'single-model-for-multiple-languages', such as determining the optimal vocabulary size of the tokenizer, avoiding token switching, and predicting the spoken language when not known beforehand. This approach enhances the model's adaptability to multiple languages .
    • VSR Translation: By leveraging the ViSpeR dataset to train models for translating visual speech from one language to another, such as from French to English, the paper opens up new possibilities for seamless communication across linguistic barriers, enhancing cross-language applications .
    • Applications Beyond VSR: The ViSpeR dataset can be utilized for lip syncing, speaker identification, and other applications in the field of audio-visual speech recognition, expanding the scope of potential uses beyond traditional VSR .

In conclusion, the ViSpeR dataset offers significant advantages over previous methods by providing a diverse, comprehensive resource for non-English VSR research, enabling the training of self-supervised and multi-lingual models, facilitating VSR translation, and supporting various applications in the AVSR domain .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of Audio-Visual Speech Recognition (AVSR). Noteworthy researchers in this field include Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Changchong Sheng, Gangyao Kuang, Liang Bai, Chenping Hou, Yulan Guo, Xin Xu, Matti Pietikäinen, Li Liu, Jeong Hun Yeo, Minsu Kim, Shinji Watanabe, Yong Man Ro, Amir Zadeh, Yan Sheng Cao, Simon Hessner, Paul Pu Liang, Soujanya Poria, Louis-Philippe Morency, Triantafyllos Afouras, Andrew Zisserman, Mohamed Anwar, Vedanuj Goswami, Juan Pino, Changhan Wang, Rosana Ardila, Megan Branson, Kelly Davis, Michael Henretty, Michael Kohler, Josh Meyer, Reuben Morais, Lindsay Saunders, Francis M Tyers, Gregor Weber, Ariel Ephrat, Inbar Mosseri, Oran Lang, Tali Dekel, Kevin Wilson, Avinatan Hassidim, William T Freeman, Michael Rubinstein, Igor S. Gruzman, Anna S. Kostenkova, Denis Ivanko, Alexandr Axyonov, Dmitry Ryumin, Alexey Kashevnik, Alexey Karpov, Pingchuan Ma, Alexandros Haliassos, Adriana Fernandez-Lopez, Honglie Chen, Stavros Petridis, Maja Pantic, Takaki Makino, Hank Liao, Yannis Assael, Brendan Shillingford, Basilio Garcia, Otavio Braga, Olivier Siohan, Sanath Narayan, Yasser Abdelaziz Dahou Djilali, Ankit Singh, Eustache Le Bihan, Hakim Hacid, Arsha Nagrani, Joon Son Chung, Weidi Xie, Andros Tjandra, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Bowen Shi, Wei-Ning Hsu, Kushal Lakhotia, Abdelrahman Mohamed, and many others .

The key to the solution mentioned in the paper "ViSpeR: Multilingual Audio-Visual Speech Recognition" is the development of a large-scale multilingual dataset called ViSpeR for Audio-Visual Speech Recognition. This dataset contains nearly 3.2 million clips with more than 3600 hours duration in total, covering languages such as Chinese, Arabic, Spanish, and French. The dataset was carefully curated from various settings like interviews and talks to ensure diversity. Additionally, the paper discusses the training of supervised VSR and AVSR models on the ViSpeR dataset, establishing them as baselines on the introduced benchmarks .


How were the experiments in the paper designed?

The experiments in the paper were designed by utilizing processed multilingual VSR video-text pairs to train an encoder-decoder model in a fully-supervised manner. The encoder-decoder model structure closely follows the state-of-the-art AutoAVSR model, with a 12-layer encoder and a 6-layer decoder. The hidden size, MLP, and number of heads are set to 768, 3072, and 12, respectively. Unigram tokenizers are learned for all languages combined with a vocabulary size of 21k. The models were trained for 150 epochs on 64 Nvidia A100 GPUs (40GB) using the AdamW optimizer with a maximum LR of 1e-3 .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is called ViSpeR . The ViSpeR dataset is a large-scale multilingual dataset for Audio-Visual Speech Recognition (AVSR) that covers four languages: Chinese, Arabic, Spanish, and French. It contains nearly 3.2 million clips with more than 3600 hours duration in total, making it a comprehensive resource for non-English VSR research .

Regarding the code, yes, the code for the ViSpeR dataset and models is open source and available on GitHub at https://github.com/YasserdahouML/visper . This open-source approach aims to serve as a foundation for further research and exploration in the field of Audio-Visual Speech Recognition, facilitating collaboration and advancement in this important area of research.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted an extensive analysis of Audio-Visual Speech Recognition (AVSR) for five widely spoken languages, including Chinese, Spanish, English, Arabic, and French . The research involved collecting large-scale datasets for each language and training supervised learning models in a multi-lingual setting, leading to competitive performance on newly established benchmarks for each language .

The experiments conducted in the study involved training encoder-decoder models in a fully-supervised manner, closely following the structure of state-of-the-art models like AutoAVSR . The models were trained under a multi-lingual setting, with specific configurations such as a 12-layer encoder, 6-layer decoder, and other parameters set to optimize performance . These experiments aimed to address the challenges posed by VSR, such as the ambiguous nature of input data and the complexities of acquiring and processing audio-visual data .

The results obtained from the experiments, as shown in Table 3, demonstrate the performance of multilingual VSR and AVSR models across different languages . The models were evaluated on proposed benchmarks, showcasing varying performance levels across languages. The study observed better model performance on Latin languages compared to non-Latin languages on both VSR and AVSR tasks . Additionally, the models performed better on the wild test split, indicating the effectiveness of the training approach and dataset curation .

Overall, the experiments and results presented in the paper provide strong empirical evidence to support the scientific hypotheses related to Audio-Visual Speech Recognition for multiple languages. The study's methodology, dataset creation, model training, and evaluation strategies contribute significantly to advancing research in this field and validating the effectiveness of the proposed approaches .


What are the contributions of this paper?

The contributions of this paper include the following key points:

  • Data Pipeline and Dataset: The paper develops an efficient data collection pipeline for Visual Speech Recognition (VSR) to gather substantial amounts of data for Chinese, Arabic, Spanish, and French languages .
  • Benchmarks: Carefully curated benchmarks are created for each language to facilitate further advancements and measurements in the field of Audio-Visual Speech Recognition (AVSR) .
  • ViSpeR Model: The study engages in training supervised VSR and AVSR models, establishing them as baselines on the introduced benchmarks, showcasing competitive performance and paving the way for future research in the domain of multilingual AVSR .

What work can be continued in depth?

The work on ViSpeR dataset opens up avenues for further research and exploration in Audio-Visual Speech Recognition (AVSR) for multiple languages. Some potential areas for continued in-depth work include:

  • Self-Supervised Learning methods for VSR: Future directions may involve training multilingual self-supervised models on the ViSpeR dataset to establish foundational models for VSR, incorporating suitable clustering methods for creating pseudo-labels to address the multi-lingual aspect .
  • Multi-lingual supervised models: Exploring the optimal vocabulary size of the tokenizer, strategies to prevent token switching when mixing languages, and methods to predict the spoken language in a 'single-model-for-multiple-languages' scenario are important questions to address .
  • VSR translation: Leveraging the dataset to train models for translating visual speech from one language to another, such as from French to English, can facilitate seamless communication across linguistic barriers .
  • Other applications: Apart from VSR, the ViSpeR dataset could be utilized for lip syncing, speaker identification, and other related applications, broadening its potential impact and utility in the field of audio-visual processing .

Introduction
Background
Data scarcity in non-English speech recognition
Importance of multilingual models
Objective
To develop a model for Chinese, Spanish, Arabic, and French
Address data collection challenges and improve performance
Applications in lip syncing, speaker identification, and self-supervised learning
Method
Data Collection
Private and Public Data Sources
Combining diverse datasets
Inclusion of TED talks and a Wild subset
Binary Classifier for Video Filtering
Filtering process to ensure quality and diversity
Data Preprocessing
Audio-visual data preprocessing techniques
Handling accent diversity and transcription quality
Model Development
ViSpeR Architecture
Description of the model's design and training process
Performance Evaluation
Comparison with previous non-English datasets
Focus on Latin languages and accent recognition
Experiments and Results
Improved performance on Latin languages
Self-supervised learning capabilities
Applications and real-world scenarios
Limitations and Ethical Considerations
Gap in audio-visual speech recognition technology
Biases in YouTube videos and their impact on model performance
Related Work
Weak supervision and deep learning in low-resource languages
Other research efforts in speech recognition and visual analysis
Conclusion
ViSpeR's contribution to multilingual speech recognition
Future directions and potential improvements
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
How does ViSpeR perform compared to previous non-English datasets, and which languages benefit the most from its performance?
What are the potential applications of the ViSpeR dataset, and what ethical considerations are raised?
What is ViSpeR, and what languages is it trained on?
How does the authors address data scarcity in the study?

ViSpeR: Multilingual Audio-Visual Speech Recognition

Sanath Narayan, Yasser Abdelaziz Dahou Djilali, Ankit Singh, Eustache Le Bihan, Hakim Hacid·May 27, 2024

Summary

This study introduces ViSpeR, a multilingual audio-visual speech recognition model trained on large datasets for Chinese, Spanish, Arabic, and French. The authors address data scarcity by creating a diverse and efficient data collection pipeline, combining private and public sources, and filtering videos through a binary classifier. ViSpeR outperforms previous non-English datasets, with a focus on TED talks and a Wild subset for diverse content. Experiments show better performance on Latin languages due to accent diversity and higher-quality transcriptions. The dataset, with over 3.2 million clips and 3600 hours of content, also addresses self-supervised learning and has applications in lip syncing and speaker identification. The work highlights the gap between audio-visual speech recognition and its limitations, while raising ethical considerations about biases in YouTube videos. Other research efforts in speech recognition and visual analysis leverage weak supervision and deep learning for low-resource languages.
Mind map
Filtering process to ensure quality and diversity
Inclusion of TED talks and a Wild subset
Combining diverse datasets
Focus on Latin languages and accent recognition
Comparison with previous non-English datasets
Description of the model's design and training process
Handling accent diversity and transcription quality
Audio-visual data preprocessing techniques
Binary Classifier for Video Filtering
Private and Public Data Sources
Applications in lip syncing, speaker identification, and self-supervised learning
Address data collection challenges and improve performance
To develop a model for Chinese, Spanish, Arabic, and French
Importance of multilingual models
Data scarcity in non-English speech recognition
Future directions and potential improvements
ViSpeR's contribution to multilingual speech recognition
Other research efforts in speech recognition and visual analysis
Weak supervision and deep learning in low-resource languages
Biases in YouTube videos and their impact on model performance
Gap in audio-visual speech recognition technology
Applications and real-world scenarios
Self-supervised learning capabilities
Improved performance on Latin languages
Performance Evaluation
ViSpeR Architecture
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Related Work
Limitations and Ethical Considerations
Experiments and Results
Model Development
Method
Introduction
Outline
Introduction
Background
Data scarcity in non-English speech recognition
Importance of multilingual models
Objective
To develop a model for Chinese, Spanish, Arabic, and French
Address data collection challenges and improve performance
Applications in lip syncing, speaker identification, and self-supervised learning
Method
Data Collection
Private and Public Data Sources
Combining diverse datasets
Inclusion of TED talks and a Wild subset
Binary Classifier for Video Filtering
Filtering process to ensure quality and diversity
Data Preprocessing
Audio-visual data preprocessing techniques
Handling accent diversity and transcription quality
Model Development
ViSpeR Architecture
Description of the model's design and training process
Performance Evaluation
Comparison with previous non-English datasets
Focus on Latin languages and accent recognition
Experiments and Results
Improved performance on Latin languages
Self-supervised learning capabilities
Applications and real-world scenarios
Limitations and Ethical Considerations
Gap in audio-visual speech recognition technology
Biases in YouTube videos and their impact on model performance
Related Work
Weak supervision and deep learning in low-resource languages
Other research efforts in speech recognition and visual analysis
Conclusion
ViSpeR's contribution to multilingual speech recognition
Future directions and potential improvements

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of Audio-Visual Speech Recognition (AVSR) by presenting an extensive study on AVSR for five widely spoken languages: Chinese, Spanish, English, Arabic, and French . This research work focuses on training supervised learning models in a multi-lingual setting to achieve competitive performance on newly established benchmarks for each language . While AVSR is an increasingly important area of research, the paper contributes by providing large-scale datasets and models to serve as a foundation for further exploration in AVSR . The problem of AVSR is not entirely new, but the paper's approach and the datasets it introduces contribute significantly to advancing research in this field .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to Audio-Visual Speech Recognition (AVSR) for five widely spoken languages: Chinese, Spanish, English, Arabic, and French. The study focuses on collecting large-scale datasets for each language and training supervised learning models in a multi-lingual setting to achieve competitive performance on newly established benchmarks for each language . The research explores the challenges in training deep learning models for Visual Speech Recognition (VSR) due to the ambiguous nature of input data, lack of large-scale datasets, and complexities in acquiring VSR data compared to Audio Speech Recognition (ASR) .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "ViSpeR: Multilingual Audio-Visual Speech Recognition" proposes several new ideas, methods, and models in the field of Audio-Visual Speech Recognition (AVSR) . Here are some key points from the paper:

  1. ViSpeR Dataset: The paper introduces the ViSpeR dataset, a large-scale multilingual dataset for Audio Visual Speech Recognition. This dataset contains nearly 3.2 million clips with over 3600 hours of total duration, covering four languages: Chinese, Arabic, Spanish, and French. The clips were carefully filtered from various settings like interviews and talks to ensure diversity in the dataset .

  2. Self-Supervised Learning for VSR: The paper suggests future directions for training multilingual self-supervised models on the ViSpeR dataset to create foundational models for VSR. This involves finding suitable clustering methods to create pseudo-labels considering the multi-lingual aspect .

  3. Multi-lingual Supervised Models: The paper discusses the challenges and questions that arise when training a 'single-model-for-multiple-languages'. It raises questions about the optimal vocabulary size of the tokenizer, avoiding token switching when mixing languages, and predicting the spoken language when not known beforehand .

  4. VSR Translation: The paper explores leveraging the ViSpeR dataset to train models for translating visual speech from one language to another, such as from French to English. This capability has the potential to facilitate seamless communication across linguistic barriers .

  5. Other Applications: Apart from VSR, the ViSpeR dataset can also be used for lip syncing, speaker identification, and other applications in the field of audio-visual speech recognition .

  6. Model Training and Baselines: The paper discusses training supervised VSR and AVSR models on the ViSpeR dataset to establish them as baselines on the introduced benchmarks. The models are trained in a multi-lingual setting, resulting in competitive performance on newly established benchmarks for each language .

In summary, the paper introduces the ViSpeR dataset, proposes future research directions for self-supervised learning in VSR, discusses challenges in training multi-lingual models, explores VSR translation capabilities, and highlights various applications of the dataset beyond VSR, contributing significantly to the field of Audio-Visual Speech Recognition . The paper "ViSpeR: Multilingual Audio-Visual Speech Recognition" introduces several characteristics and advantages compared to previous methods in the field of Audio-Visual Speech Recognition (AVSR) .

  1. ViSpeR Dataset Characteristics:

    • Scale and Coverage: The ViSpeR dataset surpasses others in scale and coverage, exhibiting substantial increases in the number of clips and total duration across all languages. This makes it a comprehensive resource for non-English VSR research .
    • Diversity: The dataset contains nearly 3.2 million clips with over 3600 hours of total duration, covering four languages: Chinese, Arabic, Spanish, and French. The clips were filtered from various settings like interviews and talks to ensure diversity .
    • Test Set Quality: To ensure fair and robust evaluations, the paper takes additional measures by obtaining a second transcription using the Seamless-M4T model for a pool of considered samples to build high-quality test sets. Clips are retained based on transcripts generated by Whisper, ensuring reliability .
  2. Advantages Compared to Previous Methods:

    • Self-Supervised Learning: The paper suggests future directions for training multilingual self-supervised models on the ViSpeR dataset to create foundational models for VSR. This approach aims to find suitable clustering methods for creating pseudo-labels to account for the multi-lingual aspect, offering a more robust training method .
    • Multi-lingual Supervised Models: The paper addresses important questions when training a 'single-model-for-multiple-languages', such as determining the optimal vocabulary size of the tokenizer, avoiding token switching, and predicting the spoken language when not known beforehand. This approach enhances the model's adaptability to multiple languages .
    • VSR Translation: By leveraging the ViSpeR dataset to train models for translating visual speech from one language to another, such as from French to English, the paper opens up new possibilities for seamless communication across linguistic barriers, enhancing cross-language applications .
    • Applications Beyond VSR: The ViSpeR dataset can be utilized for lip syncing, speaker identification, and other applications in the field of audio-visual speech recognition, expanding the scope of potential uses beyond traditional VSR .

In conclusion, the ViSpeR dataset offers significant advantages over previous methods by providing a diverse, comprehensive resource for non-English VSR research, enabling the training of self-supervised and multi-lingual models, facilitating VSR translation, and supporting various applications in the AVSR domain .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of Audio-Visual Speech Recognition (AVSR). Noteworthy researchers in this field include Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Changchong Sheng, Gangyao Kuang, Liang Bai, Chenping Hou, Yulan Guo, Xin Xu, Matti Pietikäinen, Li Liu, Jeong Hun Yeo, Minsu Kim, Shinji Watanabe, Yong Man Ro, Amir Zadeh, Yan Sheng Cao, Simon Hessner, Paul Pu Liang, Soujanya Poria, Louis-Philippe Morency, Triantafyllos Afouras, Andrew Zisserman, Mohamed Anwar, Vedanuj Goswami, Juan Pino, Changhan Wang, Rosana Ardila, Megan Branson, Kelly Davis, Michael Henretty, Michael Kohler, Josh Meyer, Reuben Morais, Lindsay Saunders, Francis M Tyers, Gregor Weber, Ariel Ephrat, Inbar Mosseri, Oran Lang, Tali Dekel, Kevin Wilson, Avinatan Hassidim, William T Freeman, Michael Rubinstein, Igor S. Gruzman, Anna S. Kostenkova, Denis Ivanko, Alexandr Axyonov, Dmitry Ryumin, Alexey Kashevnik, Alexey Karpov, Pingchuan Ma, Alexandros Haliassos, Adriana Fernandez-Lopez, Honglie Chen, Stavros Petridis, Maja Pantic, Takaki Makino, Hank Liao, Yannis Assael, Brendan Shillingford, Basilio Garcia, Otavio Braga, Olivier Siohan, Sanath Narayan, Yasser Abdelaziz Dahou Djilali, Ankit Singh, Eustache Le Bihan, Hakim Hacid, Arsha Nagrani, Joon Son Chung, Weidi Xie, Andros Tjandra, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Bowen Shi, Wei-Ning Hsu, Kushal Lakhotia, Abdelrahman Mohamed, and many others .

The key to the solution mentioned in the paper "ViSpeR: Multilingual Audio-Visual Speech Recognition" is the development of a large-scale multilingual dataset called ViSpeR for Audio-Visual Speech Recognition. This dataset contains nearly 3.2 million clips with more than 3600 hours duration in total, covering languages such as Chinese, Arabic, Spanish, and French. The dataset was carefully curated from various settings like interviews and talks to ensure diversity. Additionally, the paper discusses the training of supervised VSR and AVSR models on the ViSpeR dataset, establishing them as baselines on the introduced benchmarks .


How were the experiments in the paper designed?

The experiments in the paper were designed by utilizing processed multilingual VSR video-text pairs to train an encoder-decoder model in a fully-supervised manner. The encoder-decoder model structure closely follows the state-of-the-art AutoAVSR model, with a 12-layer encoder and a 6-layer decoder. The hidden size, MLP, and number of heads are set to 768, 3072, and 12, respectively. Unigram tokenizers are learned for all languages combined with a vocabulary size of 21k. The models were trained for 150 epochs on 64 Nvidia A100 GPUs (40GB) using the AdamW optimizer with a maximum LR of 1e-3 .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is called ViSpeR . The ViSpeR dataset is a large-scale multilingual dataset for Audio-Visual Speech Recognition (AVSR) that covers four languages: Chinese, Arabic, Spanish, and French. It contains nearly 3.2 million clips with more than 3600 hours duration in total, making it a comprehensive resource for non-English VSR research .

Regarding the code, yes, the code for the ViSpeR dataset and models is open source and available on GitHub at https://github.com/YasserdahouML/visper . This open-source approach aims to serve as a foundation for further research and exploration in the field of Audio-Visual Speech Recognition, facilitating collaboration and advancement in this important area of research.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted an extensive analysis of Audio-Visual Speech Recognition (AVSR) for five widely spoken languages, including Chinese, Spanish, English, Arabic, and French . The research involved collecting large-scale datasets for each language and training supervised learning models in a multi-lingual setting, leading to competitive performance on newly established benchmarks for each language .

The experiments conducted in the study involved training encoder-decoder models in a fully-supervised manner, closely following the structure of state-of-the-art models like AutoAVSR . The models were trained under a multi-lingual setting, with specific configurations such as a 12-layer encoder, 6-layer decoder, and other parameters set to optimize performance . These experiments aimed to address the challenges posed by VSR, such as the ambiguous nature of input data and the complexities of acquiring and processing audio-visual data .

The results obtained from the experiments, as shown in Table 3, demonstrate the performance of multilingual VSR and AVSR models across different languages . The models were evaluated on proposed benchmarks, showcasing varying performance levels across languages. The study observed better model performance on Latin languages compared to non-Latin languages on both VSR and AVSR tasks . Additionally, the models performed better on the wild test split, indicating the effectiveness of the training approach and dataset curation .

Overall, the experiments and results presented in the paper provide strong empirical evidence to support the scientific hypotheses related to Audio-Visual Speech Recognition for multiple languages. The study's methodology, dataset creation, model training, and evaluation strategies contribute significantly to advancing research in this field and validating the effectiveness of the proposed approaches .


What are the contributions of this paper?

The contributions of this paper include the following key points:

  • Data Pipeline and Dataset: The paper develops an efficient data collection pipeline for Visual Speech Recognition (VSR) to gather substantial amounts of data for Chinese, Arabic, Spanish, and French languages .
  • Benchmarks: Carefully curated benchmarks are created for each language to facilitate further advancements and measurements in the field of Audio-Visual Speech Recognition (AVSR) .
  • ViSpeR Model: The study engages in training supervised VSR and AVSR models, establishing them as baselines on the introduced benchmarks, showcasing competitive performance and paving the way for future research in the domain of multilingual AVSR .

What work can be continued in depth?

The work on ViSpeR dataset opens up avenues for further research and exploration in Audio-Visual Speech Recognition (AVSR) for multiple languages. Some potential areas for continued in-depth work include:

  • Self-Supervised Learning methods for VSR: Future directions may involve training multilingual self-supervised models on the ViSpeR dataset to establish foundational models for VSR, incorporating suitable clustering methods for creating pseudo-labels to address the multi-lingual aspect .
  • Multi-lingual supervised models: Exploring the optimal vocabulary size of the tokenizer, strategies to prevent token switching when mixing languages, and methods to predict the spoken language in a 'single-model-for-multiple-languages' scenario are important questions to address .
  • VSR translation: Leveraging the dataset to train models for translating visual speech from one language to another, such as from French to English, can facilitate seamless communication across linguistic barriers .
  • Other applications: Apart from VSR, the ViSpeR dataset could be utilized for lip syncing, speaker identification, and other related applications, broadening its potential impact and utility in the field of audio-visual processing .
Scan the QR code to ask more questions about the paper
© 2025 Powerdrill. All rights reserved.