Leveraging Pre-training Models for Speech Processing

Introduction

Pre-training has proven to be crucial in advancing the state of speech, natural language processing (NLP), and computer vision (CV) research in recent years. The network is first trained via a pre-training task, leveraging ubiquitous unlabeled data. The pre-training is usually application-agnostic, and the pre-trained models are transferred to multiple downstream applications. SUPERB [Yang et al., 2021], LeBenchmark [Evain et al., 2021], and NOSS [Shor et al., 2020][Shor et al., 2021] are the initial effort to evaluate the pre-trained models on their generalizability across various speech and audio processing tasks and find that the pre-trained models achieve outstanding performance on a wide range of tasks. The public available pre-trained models significantly benefit the small players. For example, with a pre-trained model, one only needs to train a two-layer LSTM as a downstream model to achieve 3% WER on Librispeech [Yang et al., 2021]. Without pre-training, a network with more than ten layers is usually required to achieve the same-level performance.

The project aims to expand the existing benchmarking to provide a comprehensive understanding of pre-trained networks and develop new techniques to leverage the pre-trained models better. We believe this project will broadly push the front of network pre-training technology in speech. We will explore the following research directions, including: 1) Expand the use of pre-trained techniques for modeling prosody. 2) Look for efficient ways to leverage the pre-trained models in downstream tasks. 3) Investigate the robustness of pre-trained models, for example, benchmarking their performance under domain mismatch, and make them more robust by pre-training with visual information. 4) Develop efficient pre-trained models regarding computation and memory (or even carbon) footprint. We will open-source developed benchmarks, tools, and techniques for all researchers interested in the area driving the frontier together.

Webpage: https://jsalt-2022-ssl.github.io
Twitter:Twitter

Research Direction

I. Pre-trained Models for Prosody. Previous work has studied the ability of pre-trained models to extract phonetic and speaker information [Yang et al., 2021][Evain et al., 2021]. However, it is unclear whether they can extract prosodic information from speech, which is essential for interactive dialogue systems. The project will first extend the existing pre-training models to support prosody for pragmatic-related classification tasks, including turn-taking, micro-emotion, and prediction of response prosody. Then we will study which kind of pre-training task is most suitable for the pre-training model to learn prosody, and we will adjust/expand the pre-training models to better handle the prosody. Outcome: Deliver the pre-trained model for prosody extraction.

II. How to use the pre-trained models. So far, there are two main methods of using the speech pre-training models: (1) freezing the representation models and using them as feature extractors, (2) fine-tuning the representation models with downstream tasks. Are there other ways to use them? In the NLP community, the adapter-based method [Guo et al., 2020][Ben Zaken et al., 2021] and prompt/instruction learning [Liu et al., 2021] have achieved competitive performance compared with fine-tuning, but the exploration in speech is insufficient. We will apply these methods to use cases such as multi-linguality, multi-accent, low-resourced learning, model robustness, understand the efficacy of these methods, and innovate ways to improve performance. Outcome: Deliver a toolbox for efficient leverage pre-trained speech model.

III. Model robustness & Visual-enhanced Pre-trained Models: The pre-training models show outstanding performance on various applications, but their failure modes are still unclear. Are they robust to domain shift? Are they robust to adversarial attacks? There are some preliminary researches about domain shift [Hsu et al.,2021] and the adversarial attack [Wu et al., 2021] of pre-training models, but we will conduct a more thorough study in this project. We will also study how to make the pre-trained models more robust. It has been known that visual information improves speech representations [Peng et al., 2021]. This project will further study the robustness of the visual-enhanced pre-trained models. Outcome: Comprehensively understand the robustness of the pre-trained models and deliver a more robust pre-trained model.

IV. Greener Pre-trained Models: Larger representation models usually lead to better downstream performance [Pu et al., 2021]. Despite the success of these vast models, they require large memory and high computational costs. We will explore various approaches of network compression, including knowledge distillation [Chang et al., 2021], pruning [Lai et al., 2021], and dynamic computation [Yu et al., 2019], etc. to compress pre-trained networks. These technologies are helpful in CV and NLP [Ganesh et al., 2021] but not fully explored in pre-trained speech models. We will optimize and provide benchmarks for metrics beyond accuracy, e.g., number of parameters and inference time. Outcome: Deliver a small yet powerful pre-trained model.

Preparation

Team members developed a public toolkit s3prl (https://github.com/s3prl/s3prl) to support pre-training research on speech processing tasks. These toolkits can support a wide range of pretrained models and downstream tasks. We are extending s3prl with more architectures, learning algorithms, and benchmark metrics, and use cases. In the workshop, we will be working on the project based on s3prl, so some pretrained models and downstream tasks for evaluation are ready.

Timeline

Heavy Pre-training (Pre-workshop)
● Preparing all pre-trained
models and downstream tasks.
● Running the standard
techniques in each direction.
Fine-tuning and Lightweight Pre-training (During workshop)
2 Weeks
● Integrating the directions (e.g., model compression may change model robustness, etc.)
Fine-tuning and Lightweight Pre-training (During workshop)
4 weeks
● Preparing the outcomes for each direction.
● Investigating new ideas for leveraging pre-training spanning the four directions.

Reference

[Ben Zaken et al., 2021] Elad Ben Zaken, and Shauli Ravfogel, Yoav Goldberg, BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-model, arXiv:2106.10199, 2021

[Chang et al., 2021] Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee, DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT, arXiv:2110.01900, 2021

[Evain et al., 2021] Solene Evain, Ha Nguyen, Hang Le, Marcely Zanon Boito, Salima Mdhaffar, Sina Alisamir, Ziyi Tong, Natalia Tomashenko, Marco Dinarelli, Titouan Parcollet, Alexandre Allauzen, Yannick Esteve, Benjamin Lecouteux, Francois Portet, Solange Rossato, Fabien Ringeval, Didier Schwab, Laurent Besacier, LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech, Interspeech, 2021

[Ganesh et al., 2021] Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Hassan Sajjad, Preslav Nakov, Deming Chen, Marianne Winslett, Compressing Large-Scale Transformer-Based Models: A Case Study on BERT, TACL, 2021

[Guo et al., 2020] Demi Guo, Alexander M Rush, Yoon Kim, Parameter-efficient transfer learning with diff pruning, arXiv:2012.07463, 2020

[Hsu et al., 2021] Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski, Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Jacob Kahn, Ann Lee, Ronan Collobert, Gabriel Synnaeve, Michael Auli, Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training, Interspeech, 2021

[Lai et al., 2021] Cheng-I Jeff Lai, Yang Zhang, Alexander H. Liu, Shiyu Chang, Yi-Lun Liao, Yung-Sung Chuang, Kaizhi Qian, Sameer Khurana, David D. Cox, James R. Glass, PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition, arXiv:2106.05933, 2021

[Liu et al., 2021] Pengfei Liu, Weizhe Yuan and Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, Graham Neubig, Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing, arXiv:2107.13586, 2021

[Pu et al., 2021] Jie Pu, Yuguang Yang, Ruirui Li, Oguz Elibol, Jasha Droppo, Scaling Effect of Self-Supervised Speech Models, Interspeech, 2021 [Peng et al., 2021] Puyuan Peng, David Harwath, Fast-Slow Transformer for Visually Grounding Speech, arXiv:2109.08186, 2021

[Shor et al., 2020] Joel Shor, Aren Jansen, Ronnie Maor, Oran Lang, Omry Tuval, Felix de Chaumont Quitry, Marco Tagliasacchi, Ira Shavitt, Dotan Emanuel, Yinnon Haviv, Towards Learning a Universal Non-Semantic Representation of Speech, Interspeech, 2020

[Shor et al., 2021] Joel Shor, Aren Jansen, Wei Han, Daniel Park, Yu Zhang, Universal Paralinguistic Speech Representations Using Self-Supervised Conformers, arXiv:2110.04621, 2021 [Wu et al., 2021] Haibin Wu, Bo Zheng, Xu Li, Xixin Wu, Hung-yi Lee, Helen Meng, Characterizing the adversarial vulnerability of speech self-supervised learning, arXiv:2111.04330, 2021

[Yang et al., 2021] Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang, Cheng-I Jeff Lai, Kushal Lakhotia, Yist Y. Lin, Andy T. Liu, Jiatong Shi, Xuankai Chang, Guan-Ting Lin, Tzu-Hsien Huang, Wei-Cheng Tseng, Ko-tik Lee, Da-Rong Liu, Zili Huang, Shuyan Dong, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-yi Lee, SUPERB: Speech Processing Universal PERformance Benchmark, Interspeech, 2021

[Yu et al., 2019] Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, Thomas Huang, Slimmable Neural Networks, ICLR, 2019

Team Leader

Hung-yi Lee

Senior Members

David Harwath
Ann Lee
Lucas Ondel
Hao Tang

Graduate Students

Layne Berry
Lea-Marie Lam-Yee-Mui
Shu-wen “Leo” Yang
Fabian Ritter
Dongji Gao
Jiatong Shi

Affiliate Members
Shang-Wen(Daniel)Li
Diego Aguirre
Yu Zhang
Nigel Ward
Shinji Watanabe

Center for Language and Speech Processing