The Autoregressive Moving Average （ARMA） model for whispered speech is proposed. with normal speech, whispered speech has no fundamental frequency because of the glottis being semi-opened and turbulent flow being cr...The Autoregressive Moving Average （ARMA） model for whispered speech is proposed. with normal speech, whispered speech has no fundamental frequency because of the glottis being semi-opened and turbulent flow being created, and formant shifting exists in the lower frequency region due to the narrowing of the tract in the false vocal fold regions and weak acoustic coupling with the aubglottal system. Analysis shows that the effect of the subglottal system is to introduce additional pole-zero pairs into the vocal tract transfer function. Theoretically, the method based on an ARMA process is superior to that based on an AR process in the spectral analysis of the whispered speech. Two methods, the least squared modified Yule-Walker likelihood estimate （LSMY） algorithm and the Frequency-Domain Steiglitz-Mcbide （FDSM） algorithm, are applied to the ARMA mfldel for the whispered speech. The performance evaluation shows that the ARMA model is much more appropriate for representing the whispered speech than the AR model, and the FDSM algorithm provides a name acorate estimation of the whispered speech spectral envelope than the LSMY algorithm with higher conputational complexity.展开更多
This study investigated how background speech affected L1 and L2 reading of Chinese English major students. English, Dutch, and Mandarin Chinese were respectively set as the second language （L2）, foreign language ...This study investigated how background speech affected L1 and L2 reading of Chinese English major students. English, Dutch, and Mandarin Chinese were respectively set as the second language （L2）, foreign language （FL）, and first language （L1） background speech conditions. Self-paced word-by-word reading paradigm was used to collect the response time （RT） of each word. The conventional analysis revealed that L1 background speech exerted the most disruptive effect on both L1 and L2 reading could be phonological and could be at the and suggested that the background speech effect stage of phonological processing of L1 and L2 reading. It also implied that L1 phonological processing could be simultaneously activated during L2 reading. Spectral analysis of ten subjects＇ reading data indicated that pink noise existed in each time series of word RT of L1 and L2 reading in each condition. It provided clear evidence that L1 and L2 reading processing are similar with different concurrent background speech.展开更多
基金supported by the Independent Innovation Foundation of Shandong University(No.2009JC004)the Natural Science Foundation of Shandong Province(No.Y2007G31)
文摘The Autoregressive Moving Average （ARMA） model for whispered speech is proposed. with normal speech, whispered speech has no fundamental frequency because of the glottis being semi-opened and turbulent flow being created, and formant shifting exists in the lower frequency region due to the narrowing of the tract in the false vocal fold regions and weak acoustic coupling with the aubglottal system. Analysis shows that the effect of the subglottal system is to introduce additional pole-zero pairs into the vocal tract transfer function. Theoretically, the method based on an ARMA process is superior to that based on an AR process in the spectral analysis of the whispered speech. Two methods, the least squared modified Yule-Walker likelihood estimate （LSMY） algorithm and the Frequency-Domain Steiglitz-Mcbide （FDSM） algorithm, are applied to the ARMA mfldel for the whispered speech. The performance evaluation shows that the ARMA model is much more appropriate for representing the whispered speech than the AR model, and the FDSM algorithm provides a name acorate estimation of the whispered speech spectral envelope than the LSMY algorithm with higher conputational complexity.
文摘This study investigated how background speech affected L1 and L2 reading of Chinese English major students. English, Dutch, and Mandarin Chinese were respectively set as the second language （L2）, foreign language （FL）, and first language （L1） background speech conditions. Self-paced word-by-word reading paradigm was used to collect the response time （RT） of each word. The conventional analysis revealed that L1 background speech exerted the most disruptive effect on both L1 and L2 reading could be phonological and could be at the and suggested that the background speech effect stage of phonological processing of L1 and L2 reading. It also implied that L1 phonological processing could be simultaneously activated during L2 reading. Spectral analysis of ten subjects＇ reading data indicated that pink noise existed in each time series of word RT of L1 and L2 reading in each condition. It provided clear evidence that L1 and L2 reading processing are similar with different concurrent background speech.
