Language Models in ASR: Enhancing Automatic Speech Recognition Systems with Multilingual and End-to-End Approaches
Automatic Speech Recognition (ASR) systems convert spoken language into written text, playing a crucial role in applications like voice assistants, transcription services, and more. Recent advancements in ASR have focused on improving performance, particularly for low-resource languages, and simplifying deployment across multiple languages.
Researchers have explored various techniques to enhance ASR systems, such as multilingual models, end-to-end (E2E) architectures, and data augmentation. Multilingual models are trained on multiple languages simultaneously, allowing knowledge transfer between languages and improving performance on low-resource languages. E2E models, on the other hand, provide a completely neural, integrated ASR system that learns more consistently from data and relies less on domain-specific expertise.
Recent studies have demonstrated the effectiveness of these approaches in various scenarios. For instance, a sparse multilingual ASR model called 'ASR pathways' outperformed dense models and language-agnostically pruned models, providing better performance on low-resource languages. Another study showed that a single grapheme-based ASR model trained on seven geographically proximal languages significantly outperformed monolingual models. Additionally, data augmentation techniques have been employed to improve ASR robustness against errors and noise.
In summary, advancements in ASR systems have focused on multilingual and end-to-end approaches, leading to improved performance and simplified deployment. These techniques have shown promising results in various applications, making ASR systems more accessible and effective for a wide range of languages and use cases.
Language Models in ASR
Language Models in ASR Further Reading
1.Learning ASR pathways: A sparse multilingual ASR model http://arxiv.org/abs/2209.05735v3 Mu Yang, Andros Tjandra, Chunxi Liu, David Zhang, Duc Le, Ozlem Kalinli2.Diacritic Recognition Performance in Arabic ASR http://arxiv.org/abs/2302.14022v1 Hanan Aldarmaki, Ahmad Ghannam3.Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling http://arxiv.org/abs/2010.06030v2 Jiahui Yu, Wei Han, Anmol Gulati, Chung-Cheng Chiu, Bo Li, Tara N. Sainath, Yonghui Wu, Ruoming Pang4.Improved Robust ASR for Social Robots in Public Spaces http://arxiv.org/abs/2001.04619v1 Charles Jankowski, Vishwas Mruthyunjaya, Ruixi Lin5.An Approach to Improve Robustness of NLP Systems against ASR Errors http://arxiv.org/abs/2103.13610v1 Tong Cui, Jinghui Xiao, Liangyou Li, Xin Jiang, Qun Liu6.End-to-End Speech Recognition: A Survey http://arxiv.org/abs/2303.03329v1 Rohit Prabhavalkar, Takaaki Hori, Tara N. Sainath, Ralf Schlüter, Shinji Watanabe7.Streaming End-to-End Bilingual ASR Systems with Joint Language Identification http://arxiv.org/abs/2007.03900v1 Surabhi Punjabi, Harish Arsikere, Zeynab Raeesy, Chander Chandak, Nikhil Bhave, Ankish Bansal, Markus Müller, Sergio Murillo, Ariya Rastrow, Sri Garimella, Roland Maas, Mat Hans, Athanasios Mouchtaris, Siegfried Kunzmann8.Multilingual Graphemic Hybrid ASR with Massive Data Augmentation http://arxiv.org/abs/1909.06522v3 Chunxi Liu, Qiaochu Zhang, Xiaohui Zhang, Kritika Singh, Yatharth Saraf, Geoffrey Zweig9.Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters http://arxiv.org/abs/2007.03001v2 Vineel Pratap, Anuroop Sriram, Paden Tomasello, Awni Hannun, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert10.Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights http://arxiv.org/abs/2106.05852v2 Devaraja Adiga, Rishabh Kumar, Amrith Krishna, Preethi Jyothi, Ganesh Ramakrishnan, Pawan GoyalLanguage Models in ASR Frequently Asked Questions
What is the role of Language Models in ASR systems?
Language Models (LMs) in ASR systems are responsible for estimating the probability of a sequence of words or phrases in a given language. They help the ASR system to predict the most likely word sequence from the acoustic input. LMs are crucial for improving the accuracy and fluency of the transcriptions generated by ASR systems.
How do multilingual models improve ASR performance?
Multilingual models are trained on multiple languages simultaneously, allowing for knowledge transfer between languages. This transfer of knowledge can improve the performance of ASR systems, particularly for low-resource languages, by leveraging the similarities and shared features between languages. As a result, multilingual models can provide better performance on a wide range of languages and use cases.
What are end-to-end (E2E) architectures in ASR systems?
End-to-end (E2E) architectures in ASR systems are neural network-based models that directly map the input acoustic signal to the output text without relying on intermediate representations or handcrafted features. E2E models simplify the ASR pipeline by integrating all components, such as acoustic and language models, into a single neural network. This approach allows the system to learn more consistently from data and reduces the reliance on domain-specific expertise.
How does data augmentation improve ASR robustness?
Data augmentation techniques in ASR systems involve artificially creating new training data by applying various transformations to the original data. These transformations can include adding noise, changing the pitch, or time-stretching the audio. By exposing the ASR system to a wider range of variations during training, data augmentation helps improve the system's robustness against errors and noise, leading to better performance in real-world scenarios.
What is the significance of recent research in ASR systems?
Recent research in ASR systems has focused on improving performance and simplifying deployment across multiple languages. Techniques such as multilingual models, end-to-end architectures, and data augmentation have shown promising results in various applications. These advancements make ASR systems more accessible and effective for a wide range of languages and use cases, including voice assistants, transcription services, and more.
What are some challenges in developing ASR systems for low-resource languages?
Developing ASR systems for low-resource languages can be challenging due to the limited availability of training data, lack of standardized orthography, and variations in dialects and accents. These factors make it difficult to train accurate and robust ASR models. However, recent advancements in multilingual models and data augmentation techniques have shown promise in addressing these challenges and improving ASR performance for low-resource languages.
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