This paper describes the speaker independent large vocabulary speech recognition system based on phoneme recognition. Phoneme recognition employs LPC cepstrum coefficients as the feature parameter and statistical distance measure between an input pattern and phoneme reference template. Using power dips of low and high frequency range, similarity to unvoiced feature and similarity to nasal feature, the consonant segments are detected. The discrimination of phonemes is performed individually for vowels, semi-vowels and consonants. Phoneme sequence which is result of phoneme recognition is matched with each item of the word dictionary and the item with the highest similarity in the dictionary is output as the recognition result. An average phoneme recognition score is 81.4% for 212 words uttered by forty speakers including males and females; 90.6% for vowels, 78.0% for semivowels and 71.9% for consonants. An average score of word recognition is 95.6% for 274 Japanese city names uttered by forty speakers.

Large vocabulary speaker-independent Japanese speech recognition system

This paper presents the development of a high-speed speaker-independent speech recognition system for a large vocabulary of Japanese words, based on phoneme recognition. The system is implemented in small size by a pair of digital signal processors and a general purpose micro-processor on three printed circuit boards. An experimental result using 212 Japanese word samples, which have been pronounced by 20 males and 20 females, showed an average word recognition rate of 95.5%. And the word recognition process completes within 0.8 second after the end of utterance.

Compact isolated word recognition system for large vocabulary

http://ieeexplore.ieee.org/iel6/8361/26343/01168315.pdf

Large vocabulary speaker-independent japanese speech recognition system

This paper describes the speaker-independent spoken word recognition system for a large size vocabulary. Speech is analyzed by the filter bank, from whose logarithmic spectrum the 11 features are extracted every 10 ms. Using the features the speech is first segmented and the primary phoneme recognition is carried out for every segment using the Bayes decision method. After correcting errors in segmentation and phoneme recognition, the secondary recognition of part of the consonants is carried out and the phonemic sequence is determined. The word dictionary item having maximum likelihood to the sequence is chosen as the recognition output. The 75.9% score for the phoneme recognition and the 92.4% score for the word recognition are obtained for the training samples in the 212 words uttered by 10 male and 10 female speakers. For the same words uttered by 30 male and 20 female speakers different from the above speakers, the 88.1% word recognition score is obtained.

Shozo Makino

Papers

A speaker independent word recognition system based on phoneme recognition