59 research outputs found

    Insights into Land Plant Evolution Garnered from the Marchantia polymorpha Genome.

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    The evolution of land flora transformed the terrestrial environment. Land plants evolved from an ancestral charophycean alga from which they inherited developmental, biochemical, and cell biological attributes. Additional biochemical and physiological adaptations to land, and a life cycle with an alternation between multicellular haploid and diploid generations that facilitated efficient dispersal of desiccation tolerant spores, evolved in the ancestral land plant. We analyzed the genome of the liverwort Marchantia polymorpha, a member of a basal land plant lineage. Relative to charophycean algae, land plant genomes are characterized by genes encoding novel biochemical pathways, new phytohormone signaling pathways (notably auxin), expanded repertoires of signaling pathways, and increased diversity in some transcription factor families. Compared with other sequenced land plants, M. polymorpha exhibits low genetic redundancy in most regulatory pathways, with this portion of its genome resembling that predicted for the ancestral land plant. PAPERCLIP

    コユウゴエ ニ モトズク セイシツ ヘンカン ノ タメノ カイゼン ギジュツ

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    博士(Doctor)工学(Engineering)奈良先端科学技術大学院大学博第891号甲第891号博士(工学)奈良先端科学技術大学院大

    Techniques for Improving Voice Conversion Based on Eigenvoices

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    One-to-Many and Many-to-One Voice Conversion Based on Eigenvoices

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    One-to-Many and Many-to-One Voice Conversion Based on Eigenvoices

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    ICASSP2007: IEEE International Conference on Acoustics, Speech, and Signal Processing, April 15-20, 2007, Honolulu, Hawaii, USA.This paper describes two flexible frameworks of voice conversion (VC), i.e., one-to-many VC and many-to-one VC. One-to-many VC realizes the conversion from a user's voice as a source to arbitrary target speakers' ones and many-to-one VC realizes the conversion vice versa. We apply eigenvoice conversion (EVC) to both VC frameworks. Using multiple parallel data sets consisting of utterance-pairs of the user and multiple pre-stored speakers, an eigenvoice Gaussian mixture model (EV-GMM) is trained in advance. Unsupervised adaptation of the EV-GMM is available to construct the conversion model for arbitrary target speakers in one-to-many VC or arbitrary source speakers in many-to-one VC using only a small amount of their speech data. Results of various experimental evaluations demonstrate the effectiveness of the proposed VC frameworks

    Eigenvoice Conversion Based on Gaussian Mixture Model

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    INTERSPEECH2006: the 9th International Conference on Spoken Language Processing (ICSLP), September 17-21, 2006, Pittsburgh, Pennsylvania, USA.This paper describes a novel framework of voice conversion (VC). We call it eigenvoice conversion (EVC). We apply EVC to the conversion from a source speaker's voice to arbitrary target speakers' voices. Using multiple parallel data sets consisting of utterance-pairs of the source and multiple pre-stored target speakers, a canonical eigenvoice GMM (EV-GMM) is trained in advance. That conversion model enables us to flexibly control the speaker individuality of the converted speech by manually setting weight parameters. In addition, the optimum weight set for a specific target speaker is estimated using only speech data of the target speaker without any linguistic restrictions. We evaluate the performance of EVC by a spectral distortion measure. Experimental results demonstrate that EVC works very well even if we use only a few utterances of the target speaker for the weight estimation
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