Mandarin speech input method for Chinese computers and a mandarin speech recognition machine
First Claim
1. A speech recognition method comprising steps of:
- segmented a training speech syllable into an initial part and a final part;
training a Continuous Hidden Markov Model (CHMM) on the initial part to create an initial part model having trained initial part model parameters;
training a CHMM on the final part to create a final part model having trained final part model parameters;
training a CHMM on the training speech syllable to create a syllable model using the trained initial part parameter values and the trained final part parameter values as starting parameters for the syllable model;
operating on an object speech sample with the syllable model;
recognizing the object speech sample as an object speech syllable based on a degree of match of the object speech sample to the syllable model;
representing the object speech sample as a Chinese character in accordance with the object speech syllable.
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Abstract
A method of inputting Chinese characters into a computer directly from Mandarin speech which recognizes a series of monosyllables by separately recognizing syllables and Mandarin tones and assembling the recognized parts to recognize the mono-syllable using Hidden Markov Models. The recognized mono-syllable is used by a Markov Chinese Language Model in a Linguistic decoder section to determine the corresponding Chinese character A Mandarin dictation machine which uses the above method, using a speech input device to receive the Mandarin speech and digitizing it so a personal computer can further process that information. A pitch frequency detector, a Voice signal pre-processing unit, a Hidden Markov Model processor, and a training facility are all attached to the personal computer to perform their associated functions of the method above.
270 Citations
10 Claims
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1. A speech recognition method comprising steps of:
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segmented a training speech syllable into an initial part and a final part; training a Continuous Hidden Markov Model (CHMM) on the initial part to create an initial part model having trained initial part model parameters; training a CHMM on the final part to create a final part model having trained final part model parameters; training a CHMM on the training speech syllable to create a syllable model using the trained initial part parameter values and the trained final part parameter values as starting parameters for the syllable model; operating on an object speech sample with the syllable model; recognizing the object speech sample as an object speech syllable based on a degree of match of the object speech sample to the syllable model; representing the object speech sample as a Chinese character in accordance with the object speech syllable. - View Dependent Claims (2, 3, 4, 5)
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6. A speech recognition apparatus for Mandarin speech including high level, high rising, low dipping and high falling lexical tones, comprising:
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a speech signal filter for receiving a speech signal and creating a filtered analog signal; an analog-to-digital (A/D) converter connected to the speech signal filter for converting the filtered analog signal to a digital speech signal; a computer connected to the A/D converter for receiving and processing the digital signal; a pitch frequency detector connected to the computer for detecting characteristics of the pitch frequency of the speech signal thereby recognizing tones in the speech signal; a speech signal pre-processor connected to the computer for detecting the endpoints of syllables of speech signals thereby defining a beginning and ending of a syllable; a Hidden Markov Model processor connected to the computer for determining degrees of match between the speech signal and a syllable model, a tone model and a language model and recognizing speech signal syllables based on the degrees of match; a training apparatus connected to the computer for training an initial part Hidden Markov model and a final part Hidden Markov model and for training a syllable model based on trained parameters of the initial part Hidden Markov model and the final part Hidden Markov model. - View Dependent Claims (7, 8)
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9. A Mandarin tone recognition method comprising steps of:
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dividing training syllable utterances into five groups according to tones of the syllable utterances; training a Hidden Markov Model (HMM) on the five groups of training syllable utterances to create a Mandarin tone model; operating on an object speech sample with the Mandarin tone model; recognizing the object speech sample as an object Mandarin tone based on a degree of match of the object speech sample to the Mandarin tone model; representing the object speech sample as a Mandarin tone in accordance with the object Mandarin tone. - View Dependent Claims (10)
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Specification