  With the development of hardware for machine learning, newer models often
come at the cost of both increased sizes and computational complexity. In
effort to improve the efficiency for these models, we apply and investigate
recent quantization techniques on speech representation learning models. The
quantization techniques were evaluated on the SUPERB benchmark. On the ASR
task, with aggressive quantization to 1 bit, we achieved 86.32% storage
reduction (184.42 -> 25.23), 88% estimated runtime reduction (1.00 -> 0.12)
with increased word error rate (7.06 -> 15.96). In comparison with
DistillHuBERT which also aims for model compression, the 2-bit configuration
yielded slightly smaller storage (35.84 vs. 46.98), better word error rate
(12.68 vs. 13.37) and more efficient estimated runtime (0.15 vs. 0.73).
