Title: TONE RECOGNITION OF CONTINUOUS MANDARINE SPEECH ASSISTED WITH PROSODIC INFORMATION
Authors: WANG, YR
CHEN, SH
電信工程研究所
電信研究中心
Institute of Communications Engineering
Center for Telecommunications Research
Issue Date: 1-Nov-1994
Abstract: In this paper, a simple recurrent neural network (SRNN) is employed to model the prosody of continuous Mandarin speech to assist tone recognition. For each syllable in continuous speech, several acoustic features carrying prosodic information are extracted and taken as inputs to the SRNN. If proper linguistic features extracted from the context of the syllable are set as output targets, the SRNN can learn to represent the prosodic state of the utterance at the syllable using its hidden nodes. Outputs of the hidden nodes then serve as additional recognition features to assist recognition of the tone of the syllable. The performance of the proposed tone recognition approach was examined by simulation on a multilayer perception (MLP)-based speaker-dependent tone recognition task. The recognition rate was improved from 91.38% to 93.10%. The SRNN prosodic model is further analyzed to exploit the linguistic meaning of prosodic states. By vector quantizing the outputs of the hidden nodes of the SRNN, a finite-state automata that roughly represents the mechanism of human prosody pronunciation can be obtained.
URI: http://dx.doi.org/10.1121/1.411274
http://hdl.handle.net/11536/2248
ISSN: 0001-4966
DOI: 10.1121/1.411274
Journal: JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
Volume: 96
Issue: 5
Begin Page: 2637
End Page: 2645
Appears in Collections:Articles


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  1. A1994PQ01800002.pdf