Unsupervised hierarchical adaptation using reliable selection of cluster-dependent parameters

• Published in: Speech communication Vol. 30; no. 4; pp. 235 - 253
• Main Authors: Chien, Jen-Tzung, Junqua, Jean-Claude
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.04.2000; Elsevier
• Subjects: Applied sciences; Confidence measure; Exact sciences and technology; Hidden Markov model; Information, signal and communications theory; Signal processing; Speaker adaptation; Speech processing; Speech recognition; Telecommunications and information theory; Unsupervised learning; Hidden Markov model; Confidence measure; Speaker adaptation; Unsupervised learning; Speech recognition; Bayes estimation; Waveform; Experimental result; Algorithm performance; Tree structure; Markov model; Hierarchical system; Speech processing; Adaptation; Confidence interval

Adaptation of speaker-independent hidden Markov models (HMMs) to a new speaker using speaker-specific data is an effective approach to improve speech recognition performance for the enrolled speaker. Practically, it is desirable to flexibly perform the adaptation without any prior knowledge or limit...