Robust acoustic domain identification with its application to speaker diarization

• Published in: International journal of speech technology Vol. 25; no. 4; pp. 933 - 945
• Main Authors: Kumar, A Kishore, Waldekar, Shefali, Sahidullah, Md, Saha, Goutam
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2022; Springer Nature B.V; Springer Verlag
• Subjects: Acoustics; Artificial Intelligence; Cluster analysis; Clustering; Computer Science; Computer Vision and Pattern Recognition; Domains; Engineering; Identification; Modules; Multimedia; Signal and Image Processing; Signal,Image and Speech Processing; Social Sciences; Sound; Jaccard error rate (JER); Agglomerative hierarchical clustering (AHC); Speaker diarization; Diarization error rate (DER); Acoustic domain identification (ADI)
• ISSN: 1381-2416; 1572-8110
• DOI: 10.1007/s10772-022-09990-9

With the rise in multimedia content over the years, more variety is observed in the recording environments of audio. An audio processing system might benefit when it has a module to identify the acoustic domain at its front-end. In this paper, we demonstrate the idea of acoustic domain identification (ADI) for speaker diarization . For this, we first present a detailed study of the various domains of the third DIHARD challenge highlighting the factors that differentiated them from each other. Our main contribution is to develop a simple and efficient solution for ADI. In the present work, we explore speaker embeddings for this task. Next, we integrate the ADI module with the speaker diarization framework of the DIHARD III challenge. The performance substantially improved over that of the baseline when the thresholds for agglomerative hierarchical clustering were optimized according to the respective domains. We achieved a relative improvement of more than 5 % and 8 % in DER for core and full conditions, respectively, on Track 1 of the DIHARD III evaluation set.