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Speech self-supervised models such as wav2vec 2.0 and HuBERT are makingrevolutionary progress in Automatic Speech Recognition (ASR). However, theyhave not been totally proven to produce better performance on tasks other thanASR. In this work, we explored partial fine-tuning and entire fine-tuning onwav2vec 2.0 and HuBERT pre-trained models for three non-ASR speech tasks:Speech Emotion Recognition, Speaker Verification and Spoken LanguageUnderstanding. With simple proposed downstream frameworks, the best scoresreached 79.58% weighted accuracy on speaker-dependent setting and 73.01%weighted accuracy on speaker-independent setting for Speech Emotion Recognitionon IEMOCAP, 2.36% equal error rate for Speaker Verification on VoxCeleb1,89.38% accuracy for Intent Classification and 78.92% F1 for Slot Filling onSLURP, showing the strength of fine-tuned wav2vec 2.0 and HuBERT on learningprosodic, voice-print and semantic representations.

A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding