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Báo cáo hóa học: " Data-Model Relationship in Text-Independent Speaker Recognition"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Data-Model Relationship in Text-Independent Speaker Recognition | EURASIP Journal on Applied Signal Processing 2005 4 471-481 2005 Hindawi Publishing Corporation Data-Model Relationship in Text-Independent Speaker Recognition John S. D. Mason School of Engineering University of Wales Swansea Swansea SA2 8 PP UK Email j. s.d.mason@swansea.ac.uk Nicholas W. D. Evans School of Engineering University of Wales Swansea Swansea SA2 8PP UK Email n.w.d.evans@swansea.ac.uk Robert Stapert Aculab Milton Keynes MK1 1PT UK Email robert.stapert@aculab.com Roland Auckenthaler School of Engineering University of Wales Swansea Swansea SA2 8PP UK Email roland@speaker-verification.com Received 12 December 2002 Revised 23 September 2004 Recommended for Publication by Kenneth Lam Text-independent speaker recognition systems such as those based on Gaussian mixture models GMMs do not include time sequence information TSI within the model itself. The level of importance of TSI in speaker recognition is an interesting question and one addressed in this paper. Recent works has shown that the utilisation of higher-level information such as idiolect pronunciation and prosodics can be useful in reducing speaker recognition error rates. In accordance with these developments the aim of this paper is to show that as more data becomes available the basic GMM can be enhanced by utilising TSI even in a text-independent mode. This paper presents experimental work incorporating TSI into the conventional GMM. The resulting system known as the segmental mixture model SMM embeds dynamic time warping DTW into a GMM framework. Results are presented on the 2000-speaker SpeechDat Welsh database which show improved speaker recognition performance with the SMM. Keywords and phrases speaker recognition segmental mixture modelling. 1. INTRODUCTION Most current state-of-the-art text-independent speaker recognition systems are based on the Gaussian mixture model GMM introduced by Reynolds 1 in 1992. The GMM can be viewed as a single state hidden Markov model HMM thus with only a