With the rise of bidirectional encoder representations from Transformer
models in natural language processing, the speech community has adopted some of
their development methodologies. Therefore, the Wav2Vec models were introduced
to reduce the data required to obtain state-of-the-art results. This work
leverages this knowledge and improves the performance of the pre-trained speech
models by simply replacing the fine-tuning dense layer with a lateral
inhibition layer inspired by the biological process. Our experiments on
Romanian, a low-resource language, show an average improvement of 12.5% word
error rate (WER) using the lateral inhibition layer. In addition, we obtain
state-of-the-art results on both the Romanian Speech Corpus and the Robin
Technical Acquisition Corpus with 1.78% WER and 29.64% WER, respectively.

使用來自 Transformer 模型的雙向編碼器表示法，在語言處理方面取得成功後，語音社區也採用了其中的一些開發方法，所以 Wav2Vec 模型被引入來減少取得最新成果所需的數據。基於該認識，本研究通過將精調的密集層替換為受生物過程啟發的側抑制層，提高了預訓練語音模型的性能。我們對羅馬尼亞語，即一種資源較低的語言進行實驗，結果顯示使用側抑制層平均可以減少 12.5% 的字錯誤率，此外，在羅馬尼亞語語音語料庫和 Robin 技術採集語料庫上實現了最新最好成果，分別為 1.78% 和 29.64% 的字錯誤率。