Transfer Learning for Speech and Language Processing

Abstract: Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and thetransfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.

• The paper provides a comprehensive review of transfer learning techniques applied to speech and language processing.
• The paper demonstrates how model adaptation methods, including DNN approaches and speaker adaptation, enhance performance in multilingual and low-resource settings.
• The study reveals that leveraging cross-domain knowledge improves model robustness and resource efficiency in deep learning frameworks.

Transfer Learning for Speech and Language Processing: A Comprehensive Survey

The paper by Dong Wang and Thomas Fang Zheng presents a thorough review of transfer learning methodologies, particularly focusing on their application in speech and language processing. The authors meticulously discuss how transfer learning, a machine learning paradigm, can mitigate data sparsity and facilitate knowledge sharing across diverse languages and domains.

Key Concepts and Methodologies

Transfer learning leverages knowledge from auxiliary resources such as data, models, or labels to benefit target tasks. This includes model adaptation methods like MAP and MLLR, prevalent in speaker adaptation scenarios. The paper categorizes transfer learning into various forms, including model adaptation, heterogeneous transfer learning, and multitask learning, among others. These categorizations are based on the similarities and differences in data and tasks between source and target domains.

Application in Speech and Language Processing

The paper underscores the vital role of transfer learning in addressing challenges inherent in speech and language processing, such as data diversity, variation, imbalance, and dynamism. Significant applications include:

1. Cross-Lingual and Multilingual Transfer: The authors explore transfer learning under multilingual settings, emphasizing how models trained in one language might apply to another. DNN-based approaches are highlighted for their ability to separate language-independent features from language-specific ones, thus reducing the resource requirement for training models in low-resource languages.
2. Speaker Adaptation: Through various techniques like speaker codes and the use of i-vectors, transfer learning helps adapt acoustic models to specific speakers. These techniques have proven effective in improving the robustness and precision of speech models.
3. Model Transfer Between Architectures: This includes learning one model from another, a technique that adapts efficiently in neural network training scenarios, especially when transitioning from simpler to more complex architectures.

Implications and Future Directions

This paper posits significant theoretical and practical implications for AI and its subfields:

• Enhanced Model Robustness: Transfer learning promotes more adaptable and resilient models by utilizing shared information across multiple tasks or domains, indicating the potential for broad applications beyond speech and language processing.
• Resource Efficiency: Especially important in settings where labeled data is scarce, transfer learning offers a path to resource-efficient model training, by leveraging data from related but distinct domains.
• Deep Learning Integration: Deep learning frameworks benefit significantly from transfer learning, unifying various methodologies through deep representation learning and further advancing speech and language technologies.

Looking ahead, advances in transfer learning could unlock capabilities in heterogeneous transfer across vastly different domains, potentially leading to breakthroughs in complex AI systems combining multi-modal inputs. Further research could refine the metrics for assessing task relatedness and improve methods to mitigate negative transfer effects.

In conclusion, while the field of transfer learning in speech and language processing is advancing rapidly, extensive opportunities remain for improving learning efficiency and adaptability. The survey by Wang and Zheng thus lays a strong foundation for future explorations in this pivotal area.