• Phonetics and Speech Sciences
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• v.10 no.1
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• pp.9-14
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• 2018

This study compared the relative contributions of intensity, F0, duration and vowel spectra of L2 English lexical stress by Japanese and Korean learners of English. Recordings of Japanese, Korean and native English speakers reading eighteen 2 to 4 syllable words in a carrier sentence were analyzed using multiple regression to investigate the influence of each acoustic correlate in determining whether a vowel was stressed. The relative contribution of each correlate was calculated by converting the coefficients to percentages. The Japanese learner group showed phonological transfer of L1 phonology to L2 lexical prosody and relied mostly on F0 and duration in manifesting L2 English stress. This is consistent with the results of the previous studies. However, advanced Japanese speakers in the group showed less reliance on F0, and more use of intensity, which is another parameter used in native English stress accents. On the other hand, there was little influence of F0 on L2 English stress by the Korean learners, probably due to the transfer of the Korean intonation pattern to L2 English prosody. Hence, this study shows that L1 transfer happens at the prosodic level for Japanese learners of English and at the intonational level for Korean learners.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.371-377
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• 2003

In this paper, comparison of feature parameters for emotion recognition using speech signal is studied. For this purpose, a corpus of emotional speech data recorded and classified according to the emotion using the subjective evaluation were used to make statical feature vectors such as average, standard deviation and maximum value of pitch and energy and phonetic feature such as MFCC parameters. In order to evaluate the performance of feature parameters speaker and context independent emotion recognition system was constructed to make experiment. In the experiments, pitch, energy parameters and their derivatives were used as a prosodic information and MFCC parameters and its derivative were used as phonetic information. Experimental results using vector quantization based emotion recognition system showed that recognition system using MFCC parameter and its derivative showed better performance than that using the pitch and energy parameters.

• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.53-59
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• 2023

Detection of children with autism spectrum disorder (ASD) based on speech has relied on predefined feature sets due to their ease of use and the capabilities of speech analysis. However, clinical impressions may not be adequately captured due to the broad range and the large number of features included. This paper demonstrates that the knowledge-driven speech features (KDSFs) specifically tailored to the speech traits of ASD are more effective and efficient for detecting speech of ASD children from that of children with typical development (TD) than a predefined feature set, extended Geneva Minimalistic Acoustic Standard Parameter Set (eGeMAPS). The KDSFs encompass various speech characteristics related to frequency, voice quality, speech rate, and spectral features, that have been identified as corresponding to certain of their distinctive attributes of them. The speech dataset used for the experiments consists of 63 ASD children and 9 TD children. To alleviate the imbalance in the number of training utterances, a data augmentation technique was applied to TD children's utterances. The support vector machine (SVM) classifier trained with the KDSFs achieved an accuracy of 91.25%, surpassing the 88.08% obtained using the predefined set. This result underscores the importance of incorporating domain knowledge in the development of speech technologies for individuals with disorders.