Microsoft has introduced UniSpeech-SAT [72] and WavLM [71] models, which follow the HuBERT
framework. These models have been designed to enhance speaker representation and improve vari-
ous downstream tasks. The key focus of these models is data augmentation during the pre-training
stage, resulting in superior performance. WavLM model has exhibited outstanding effectiveness in
diverse downstream tasks, such as automatic speech recognition, phoneme recognition, speaker

inputsequence outputsoftmaxdiscrete tokensmasked  timestepsinputsequence Acoustic  Unit DiscoveryK-means on MFCCmasked  timesteps(a)(b)A Review of Deep Learning Techniques for Speech Processing

39

Table 4. Summary of predictive self-supervised approaches and proposed models for speech processing with
associated metrics and training Data. ASR: Automatic Speech Recognition, PR: Phoneme Recognition. PC:
Phoneme Classification, SR: Speaker Recognition, LL: LibriLight, LS: LibriSpeech.

Model

Reference Task (Metric)
AReviewofDeepLearningTechniquesforSpeechProcessing