Tone Recognition of Continuous Mandarin Speech Based on Neural Networks

Sin-Horng Chen, Yih-Ru Wang

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

Abstract

Several neural network-based tone recognition schemes for Continuous Mandarin speech are discussed. A basic MLP tone recognizer using recognition features extracted from the processing syllable is first introduced. Then, some additional features extracted from neighboring syllables are added to compensate for the coarticulation effect. It is then further improved to compensate for the effect of sandhi rules of tone pronunciation by including tone information of neighboring syllables. The recognition criterion is now changed to find the best tone sequence that minimizes the total risk that simultaneously considers tone recognition of all syllables in the input utterance. Last, two approaches using HCNN and HSMLP, respectively, to model the intonation pattern as a hidden Markov chain for assisting tone recognition are proposed. The effectiveness of these schemes was confirmed by simulations on a speaker-independent tone recognition task. A recognition rate of 86.72% was achieved.

Original languageEnglish
Pages (from-to)146-150
Number of pages5
JournalIEEE Transactions on Speech and Audio Processing
Volume3
Issue number2
DOIs
StatePublished - 1 Jan 1995

Fingerprint Dive into the research topics of 'Tone Recognition of Continuous Mandarin Speech Based on Neural Networks'. Together they form a unique fingerprint.

Cite this