1997 IEEE International Conference on Acoustics, Speech, and Signal Processing

DOI: 10.1109/icassp.1997.595491

|View full text |Cite

|

Sign up to set email alerts

|

A neural network based speech recognition system for isolated Cantonese syllables

¹

,

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

0

Mentioning

0

Contrasting

0

Publication Types

Select...

Article2

Other2

Relationship

Self Cite1

Independent3

Authors

Journals

Cited by 4 publications

References 7 publications

Supporting

0

Mentioning

0

Contrasting

0

Order By: Relevance

Modular recurrent neural networks for Mandarin syllable recognition

¹

,

²

1998

IEEE Trans. Neural Netw.

View full text Add to dashboard Cite

No abstract

Modular recurrent neural networks for Mandarin syllable recognition

¹

,

²

1998

IEEE Trans. Neural Netw.

View full text Add to dashboard Cite

No abstract

Cantonese syllable recognition using neural networks

¹

,

1999

IEEE Trans. Speech Audio Process.

Self Cite

View full text Add to dashboard Cite

This work describes a novel neural network based speech recognition system for isolated Cantonese syllables. Since Cantonese is a monosyllabic and tonal language, the recognition system is composed of two major components, namely the tone recognizer and the base syllable recognizer. The tone recognizer adopts the architecture of multilayer perceptron in which each output neuron represents a particular tone. The base syllable recognizer consists of a large number of independently trained recurrent networks, each representing a designated Cantonese syllable. An integrated recognition algorithm is developed to give the ultimate recognition results based on N-best outputs of the two subrecognizers. To demonstrate the effectiveness of the proposed methods, a speaker-dependent recognition system has been built with the vocabulary expanding progressively from 40 syllables to 200 syllables. In the case of 200 syllables, a top-1 recognition accuracy of 81.8% has been attained whilst the top-3 accuracy is 95.2%.

Development of a large vocabulary speech database for Cantonese

¹

,

Chow²,

Lee³

et al.

1997 IEEE International Conference on Acoustics, Speech, and Signal Processing

View full text Add to dashboard Cite

No abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Product

Browser Extension Assistant by scite Citation Statement Search Reference Check Visualizations Dashboards Explore Journals Explore Organizations Explore Funders Embedding Badge Embedding Citation Search Pricing

Resources

Blog Help & FAQ Accessibility Statement API Terms For Universities & Governments For Researchers For Publishers For Corporate, Pharma & Enterprise Author Marketing Become an Affiliate Get an organization trial or quote scite Data & Services

About

News & Press Careers Read our Paper Coverage

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Copyright © 2024 scite LLC. All rights reserved.

Made with 💙 for researchers

Part of the Research Solutions Family.