Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders (1910.12638v2)

Abstract: We present Mockingjay as a new speech representation learning approach, where bidirectional Transformer encoders are pre-trained on a large amount of unlabeled speech. Previous speech representation methods learn through conditioning on past frames and predicting information about future frames. Whereas Mockingjay is designed to predict the current frame through jointly conditioning on both past and future contexts. The Mockingjay representation improves performance for a wide range of downstream tasks, including phoneme classification, speaker recognition, and sentiment classification on spoken content, while outperforming other approaches. Mockingjay is empirically powerful and can be fine-tuned with downstream models, with only 2 epochs we further improve performance dramatically. In a low resource setting with only 0.1% of labeled data, we outperform the result of Mel-features that uses all 100% labeled data.

• The paper introduces a Masked Acoustic Model that uses deep bidirectional Transformers to predict masked speech frames.
• It demonstrates a 35.2% improvement in phoneme classification and efficient performance with only two epochs of fine-tuning.
• Its robust unsupervised framework shows promise for advancing applications in ASR, voice conversion, and low-resource speech tasks.

An Examination of "MOCKINGJAY: UNSUPERVISED SPEECH REPRESENTATION LEARNING WITH DEEP BIDIRECTIONAL TRANSFORMER ENCODERS"

The paper "MOCKINGJAY: UNSUPERVISED SPEECH REPRESENTATION LEARNING WITH DEEP BIDIRECTIONAL TRANSFORMER ENCODERS" introduces a novel approach for unsupervised speech representation learning leveraging deep bidirectional Transformer encoders. Traditional methods in speech representation often involve autoregressive or unidirectional models, focusing typically on conditioning on past frames to predict future frames. This research, however, shifts to using the characteristic bi-directionality of Transformers, thereby enabling models to leverage both past and future context for predicting current speech frames, signifying an architectural advancement over previous techniques.

Methodological Innovation and Numerical Results

Mockingjay's approach, named after its capacity to 'mimic' sound, involves the Masked Acoustic Model (MAM) task. This task randomly masks a portion of input frames, requiring the model to predict the original content based on the surrounding unmasked frames. Such unsupervised pre-training yields a robust speech representation that shows substantial improvements in various SLP (Speech and Language Processing) tasks. Empirically, the Mockingjay architecture has demonstrated superior performance in phoneme classification, speaker recognition, and sentiment classification.

The authors report bold numerical outcomes: Mockingjay representations exhibit an absolute improvement of 35.2% in phoneme classification accuracy over traditional log Mel-features. When fine-tuned for only two epochs, the model yields startling improvements, showcasing its efficiency. The paper further emphasizes the efficacy of this approach in low-resource settings, where it significantly outperforms fully supervised Mel-feature approaches even with only 0.1% of labeled data available.

Theoretical Implications and Future Directions

Theoretically, Mockingjay advances the premise that transformer-based architectures can be adeptly harnessed for continuous data like speech, which traditionally have been aligned more closely with discrete data tasks in NLP. This investigation into continuous data augurs a shift in priorities within the field of speech representation learning, potentially guiding future endeavors to mimic this bi-directional contextual analysis approach.

Regarding future developments, the paper alludes to potential extensions into related domains of Automatic Speech Recognition (ASR), voice conversion, and speech translation. Such adaptability and robustness of Mockingjay may well inspire further explorations into the deployment of the framework in more specialized and varied SLP tasks. The ongoing optimization of its methodological approach and understanding of its transferability across domains will be critical for its application in widespread, practical scenarios.

Conclusion

In conclusion, "MOCKINGJAY: UNSUPERVISED SPEECH REPRESENTATION LEARNING WITH DEEP BIDIRECTIONAL TRANSFORMER ENCODERS" contributes significantly to speech representation learning methodologies by harnessing the power of bidirectional transformer encoders. The strong empirical results underline its utility and promise that Mockingjay functions effectively across a variety of tasks, showcasing the potential for substantial improvements in domains demanding intuitive linguistic and acoustic analysis. This research could very well set a new course in unsupervised learning within the speech domain, encouraging further explorations and optimizations of bidirectional architectures for complex, real-world speech processing applications.