<![CDATA[ABSTRAKTeknologi Automatic Speech Recognition (ASR) telah berkembang pesat sebagai alat untuk meningkatkan aksesibilitas informasi bagi penyandang tuna rungu, terutama melalui video. WAV2Vec 2.0, salah satu teknologi ASR unggulan, efektif dalam transkripsi teks, namun kinerjanya menurun saat menghadapi noise. Penelitian ini bertujuan mengoptimalkan WAV2Vec 2.0 dengan menerapkan Spectral Masking untuk mengurangi noise tanpa mengorbankan kejelasan sinyal utama. Evaluasi dilakukan pada tiga jenis video: podcast, video dengan background noise, dan video dengan background music. Hasil menunjukkan penurunan Word Error Rate (WER) yang signifikan, sebesar 78.06% pada podcast dan 53.85% pada video dengan background noise. Hasil penelitian menunjukkan bahwa Spectral Masking efektif dalam meningkatkan akurasi transkripsi, menawarkan solusi inovatif untuk aksesibilitas tuna rungu dalam kondisi audio yang kompleks.Kata kunci: noise reduction, spectral masking, tuna rungu, WAV2Vec 2.0 ABSTRACTAutomatic Speech Recognition (ASR) technology has rapidly evolved as a tool to enhance information accessibility for the hearing impaired, particularly through video content. WAV2Vec 2.0, a leading ASR technology, is effective in text transcription, but its performance degrades in the presence of noise. This study aims to optimize WAV2Vec 2.0 by applying Spectral Masking to reduce noise without compromising the clarity of the main signal. The evaluation was conducted on three types of videos: podcasts, videos with background noise, and videos with background music. The results show a significant reduction in Word Error Rate (WER), with a 78.06% decrease in podcasts and a 53.85% decrease in videos with background noise. These findings demonstrate that Spectral Masking effectively enhances transcription accuracy, offering an innovative solution for improving accessibility for the hearing impaired in complex audio conditions.Keywords: noise reduction, spectral masking, tuna rungu, WAV2Vec 2.0]]>
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