Current status and prospects of speech processing technology in Korea

Ann, Souguil

doi:10.1109/sipnn.1994.344948

Cited by 1 publication

(1 citation statement)

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“…l(9) = 1=(1 + e 019 ) (4) and the discriminative training is carried out by applying the technique of generalized probabilistic descent to minimize l(9) [25]. Detailed description of the training algorithm can be found in [11] and [12].…”

Section: Base Syllable Recognition Using Rnn'smentioning

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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Section: Base Syllable Recognition Using Rnn'smentioning

confidence: 99%