(31)

(32)

78

Mehrish et al.

is more computationally efficient, the authors were able to achieve better performance while using
fewer resources. In this approach, the ConvAdapter is added to the same location as the Bottleneck
Adapter (Figure 17).

Table 13. The study evaluated various parameter-efficient training methods on pre-trained Word2Vec 2.0,
including full fine-tuning, on the SURE benchmark. The fraction of trainable parameters were represented by
percentages, with the number of KS task’s trainable parameters given. Results are reported using weighted-f1
as the metric (w-f1) on MELD, with the best performance in bold and the second best underlined. To avoid
data imbalance, the researchers opted for using weighted-f1 as the metric. The study cites Li et al. (2023)
[315] as a reference.

Method

#Parameters

SER (acc % / w-f1) ↑
ESD

MELD

SR (acc %) ↑
ESD VCTK

ASR (wer) ↓
FLEURS

KS (acc %) ↑

LS

Speech Command

Fine Tuning
Adapter
Prefix Tuning
LoRA
ConvAdapter
AReviewofDeepLearningTechniquesforSpeechProcessing