Informed Single-Channel Speech Separation Using HMM–GMM User-Generated Exemplar Source

Wang, Qi, Woo, Wai Lok and Dlay, Satnam (2014) Informed Single-Channel Speech Separation Using HMM–GMM User-Generated Exemplar Source. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22 (12). pp. 2087-2100. ISSN 2329-9290

Full text not available from this repository.
Official URL:


We present a new approach for solving the single channel speech separation with the aid of an user-generated exemplar source that is recorded from a microphone. Our method deviates from the conventional model-based methods, which highly rely on speaker dependent training data. We readdress the problem by offering a new approach based on utterance dependent patterns extracted from the user-generated exemplar source. Our proposed approach is less restrictive, and does not require speaker dependent information and yet exceeds the performance of conventional model-based separation methods in separating male and male speech mixtures. We combine general speaker-independent (SI) features with specifically generated utterance-dependent (UD) features in a joint probability model. The UD features are initially extracted from the user-generated exemplar source and represented as statistical estimates. These estimates are calibrated based on information extracted from the mixture source to statistically represent the target source. The UD probability model is subsequently generated to target problems of ambiguity and to offer better cues for separation. The proposed algorithm is tested and compared with recent method using the GRID database and the Mocha-TIMIT database.

Item Type: Article
Uncontrolled Keywords: Concurrent pitch tracking, exemplar assistance, factorial hidden Markov model (FHMM), Gaussian mixture model (GMM), informed Source Separation (ISS), single-channel source separation (SCSS), speaker-assisted source separation
Subjects: G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 12 Apr 2019 09:19
Last Modified: 10 Oct 2019 20:04

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics