Self-supervised Representation Learning for Speech Processing

Abstract: 

Although Deep Learning models have revolutionized the speech and audio processing field, they forced building specialist models for individual tasks and application scenarios. Deep neural models also bottlenecked dialects and languages with limited labeled data.

Self-supervised representation learning methods promise a single universal model to benefit a collection of tasks and domains. They recently succeeded in NLP and computer vision domains, reaching new performance levels while reducing required labels for many downstream scenarios. Speech representation learning is experiencing similar progress with three main categories: generative, contrastive, predictive. Other approaches relied on multi-modal data for pre-training, mixing text or visual data streams with speech. This talk will present self-supervised speech representation learning approaches and their connection to related research areas. Since many of the current methods focused solely on automatic speech recognition as a downstream task, we will review recent efforts on benchmarking learned representations to extend the application of such representations beyond speech recognition.

Bio: 

Abdelrahman Mohamed (PhD) is a research scientist at Meta AI Research (previously, Facebook AI Research (FAIR)). He was a principal scientist/manager in Amazon Alexa and a researcher in Microsoft Research. Abdelrahman was part of the team that started the Deep Learning revolution in Spoken Language Processing in 2009. He is the recipient of the IEEE Signal Processing Society Best Journal Paper Award for 2016. His current research interest focuses on improving, using, and benchmarking learned speech representations, e.g. HuBERT, Wav2vec 2.0, TextlessNLP, and SUPERB.

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