Towards an Intelligent Acoustic Front End for Automatic Speech Recognition: Built-in Speaker Normalization. (19th June 2008)

• Record Type: Journal Article
• Authors: Yapanel, Umit H.; Hansen, John H. L.
• Other Names: Kuo Sen M. Academic Editor.
• Is Part Of: EURASIP journal on audio, speech, and music processing. Volume 2008(2008)
• Publication Date: 2008-06-19
• Subjects: Sound -- Recording and reproducing -- Digital techniques -- Periodicals; Computer sound processing -- Periodicals; Computer sound processing; Sound -- Recording and reproducing -- Digital techniques; Periodicals; Electronic journal; Electronic journals; 620.2
• DOI: 10.1155/2008/148967
• Languages: English
• ISSNs: 1687-4714

Abstract

Abstract : A proven method for achieving effective automatic speech recognition (ASR) due to speaker differences is to perform acoustic feature speaker normalization . More effective speaker normalization methods are needed which require limited computing resources for real-time performance. The most popular speaker normalization technique is vocal-tract length normalization (VTLN), despite the fact that it is computationally expensive. In this study, we propose a novel online VTLN algorithm entitled built-in speaker normalization (BISN), where normalization is performed on-the-fly within a newly proposed PMVDR acoustic front end. The novel algorithm aspect is that in conventional frontend processing with PMVDR and VTLN, two separating warping phases are needed; while in the proposed BISN method only one single speaker dependent warp is used to achieve both the PMVDR perceptual warp and VTLN warp simultaneously. This improved integration unifies the nonlinear warping performed in the front end and reduces simultaneously. This improved integration unifies the nonlinear warping performed in the front end and reduces computational requirements, thereby offering advantages for real-time ASR systems. Evaluations are performed for (i) an in-car extended digit recognition task, where an on-the-fly BISN implementation reduces the relative word error rate (WER) by 24%, and (ii) for a diverse noisy speech task (SPINE 2), where the relative WER improvement was 9%, both relative to the baseline speaker normalization method.