高橋 徹

A continuous vocal imitation system was developed using a computational model that explains the process of phoneme acquisition by infants. Human infants perceive speech sounds not as discrete phoneme sequences but as continuous acoustic signals. One of critical problems in phoneme acquisition is the design for segmenting these continuous speech sounds. The key idea to solve this problem is that articulatory mechanisms such as the vocal tract help human beings to perceive speech sound units corresponding to phonemes. To segment acoustic signal with articulatory movement, we apply the segmenting method to our system by Recurrent Neural Network with Parametric Bias (RNNPB). This method determines the multiple segmentation boundaries in a temporal sequence using the prediction error of the RNNPB model, and the PB values obtained by the method can be encoded as kind of phonemes. Our system was implemented by using a physical vocal tract model, called the Maeda model. Experimental results demonstrated that our system can self-organize the same phonemes in different continuous sounds, and can imitate vocal sound involving arbitrary numbers of vowels using the vowel space in the RNNPB. This suggests that our model reflects the process of phoneme acquisition.

(発表日 2 Sept.)

A very high quality speech analysis, modification and synthesis system - STRAIGHT - has now been implemented in C language and operated in realtime. This article first provides a brief summary of STRAIGHT components and then introduces the underlying principles that enabled realtime operation. In STRAIGHT, the built-in extended pitch synchronous analysis, which does not require analysis window alignment, plays an important role in realtime implementation. A detailed description of the processing steps, which are based on the so-called "just-in-time" architecture, is presented. Further, discussions on other issues related to realtime implementation and performance measures are also provided. The software will be available to researchers upon request. © 2007 The Acoustical Society of Japan.

A simple, efficient, and high-quality speech style conversion algorithm is proposed based on STRAIGHT. A very highquality VOCODER STRAIGHT consists of instantaneousfrequency based F0 and source information extraction part and F0-adaptive time-frequency smoothing part to eliminate preriodicity interferences. The proposed method uses only vowel information to design the desired conversion functions and parameters. So, it is possible to reduce the amount of training data required for conversion. The processing of the proposed method is : 1) to produce abstract spectra that is represented on the perceptual frequency axis and is derived as average spectrum for each vowel and each style; 2) to decompose the original spectrum into the abstract spectrum and the residual, fine structure; 3) to replace the abstract spectrum from the original to the target style; 4) to map the fine structure with nonlinear frequency warping for adapting the target style fine structure; 5) then to add them together to produce target speech. An efficient algorithm for this conversion was developed using an orthogonal transformation referred to as warped-DCT. An informal listening test indicated that the proposed method yields more natural and high-quality speech style conversion than the previous methods.