From phonology and acoustic properties to automatic recognition of Cantonese

Ching, P.C.; Lee, T.; Zee, Eric

doi:10.1109/sipnn.1994.344949

Cited by 11 publications

(2 citation statements)

References 8 publications

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“…Automatic speech recognition in Mandarin and English has already been studied by many researchers [3][4], but code-switching between these two languages is not much applied in ASR [5]. There have been two different approaches to code-switching recognition.…”

Section: Related Workmentioning

confidence: 99%

Code-switching Speech Detection Method by Combination of Language and Acoustic Information

Zhang¹

2012

Proceedings of the 2012 2nd International Conference on Computer and Information Applications (ICCIA 2012)

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In this paper, we propose a new speech detection method to English-Mandarin code-switching speech. Unlike previous methods, in this method we first train a support vector machine (SVM) model based on feature parameters and Gaussian Mixture Model (GMM) , then integrate the language identification (LID) information based on SVM model and acoustic information into the decoding process. Lastly, we develop a prototype system to present the method. Experiments proved that our method we can improve the accurancy of code-switching speech recognition at a certain degree compared with previous methods.

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Section: Related Workmentioning

confidence: 99%

Code-switching Speech Detection Method by Combination of Language and Acoustic Information

Zhang¹

2012

Proceedings of the 2012 2nd International Conference on Computer and Information Applications (ICCIA 2012)

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“…Recently, speech recognition research has become highly internationalized and covers multiple languages. Speech technologists in different countries are making tremendous efforts to develop recognition systems for their own languages [3]- [6]. This paper will describe an unprecedented study on speech recognition for Cantonese, which is one of the major Chinese dialects.…”

Section: Introductionmentioning

confidence: 99%

Cantonese syllable recognition using neural networks

Lee

Ching²

1999

IEEE Trans. Speech Audio Process.

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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%.

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Spoken language resources for Cantonese speech processing

Lee

Ching

et al. 2002

Speech Communication

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From phonology and acoustic properties to automatic recognition of Cantonese

Cited by 11 publications

References 8 publications

Code-switching Speech Detection Method by Combination of Language and Acoustic Information

Code-switching Speech Detection Method by Combination of Language and Acoustic Information

Cantonese syllable recognition using neural networks

Spoken language resources for Cantonese speech processing

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