音声スペクトルの概略形とその動特性を利用した単語音声認識システム

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This paper describes on the outline and the perfomance of the newly developed spoken word recognition system. In the system, the spectral local peaks and the gross parameters of speech spectrum are utilized for the phoneme recognition, and the word dictionary written in phonemic symbols is used for the last step of the word recognition. The uses of the spectral local peaks and the word dictionary are based on the previously proposed ideas [1] and experiments [2], and the use of the gross parameters of spectrum is newly added for improving the segmentation and the phoneme recognition. The schematic diagram of the system is shown in Fig. 1. The input spoken word is, first, frequencyanalyzed by the 29 channel filter bank composed of single tuned filters of Q=6, of which the center frequencies are arranged every 1/6 octave from 250Hz to 6300Hz. A least squares fit line is computed from the logarithmic analyzed spectrum every 10 ms. Then the modified spectrum is computed, which is the difference between the analyzed spectrum and the fit line. By using the fit line, the difference in the slope of speech spectrum caused by the individuality of speakers can be neglected. From the modified speech spectrum, three major local peaks are extracted, and the newly defined acoustical parameters V, G and H are computed, which express the gross pattern of the spectrum. The power W is also computed from the original analysed spectrum. The smoothed parameters of W, V, G and H are named W_s, V_s, G_s and H_s. Consonant segments are extracted by using the dynamic characteristics of V_s, W_s and V from speech. In the consonant segments, consonants or consonant groups are recognized by using the peaks and the power. Nasal is recognized by using the peaks independent of the consonant segments. Semivowels are recognized by using the dynamic characteristic of G_s, and H_s, and the peaks. If some phonemes are recognized in the same segment, only one phoneme is recognized according to priority. Vowels are recognized in the remaining segments by using the peaks. Fig. 4 shows an example of phoneme recognition. The phonemic sequence is constructed from the results of the phoneme recongnition. Some errors in the sequence are corrected by using the phoneme connecting rules. The similarity of the phonemic sequence to every items of the word dictionary in the system is computed by considering the probabilities of the additions, the omissions and erroneous phonemes with the algorithm of the dynamic programming. The item of the dictionary having maximum similarity to the sequences is chosen as the output of the word recognition. Some recognition experiments were carried out with the system. In the experiments, one item of the word dictionary corresponded to one word, and was written in the orthographical form which was easily converted from Japanese Kana letters by some rules. Table 4 shows the scores in the experiments. The score of the word recognition was found to be 83. 2% for 166 city names uttered by 15 male speakers. In the experiments using small number of words, the scores were found to be 93. 6% and 96. 3% for 51 and 20 city names, respectively.
社団法人日本音響学会の論文
1978-03-01

音声スペクトルの概略形とその動特性を利用した単語音声認識システム

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