Clustering Speech Utterances by Speaker Using Eigenvoice-Motivated Vector Space Models
Wei-Ho Tsai, Shih-Sian Cheng, Yi-Hsiang Chao, Hsin‐Min Wang · 2006
The paper investigates the problem of automatically grouping unknown speech utterances based on their associated speakers. The proposed method utilizes the vector space model, which was originally developed in document-retrieval research, to characterize each utterance as a tf-idf-based vector of acoustic terms, thereby deriving a reliable measurement of similarity between utterances. To define the required acoustic terms that are most representative in terms of voice characteristics, the Eigenvoice approach is applied to the utterances to be clustered, which creates a set of eigenvector-based terms. To further improve speaker-clustering performance, the proposed method encompasses a mechanism of blind relevance feedback for refining the inter-utterance similarity measure.