The Experts below are selected from a list of 27417 Experts worldwide ranked by ideXlab platform
Ren-hua Wang - One of the best experts on this subject based on the ideXlab platform.
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Building HMM based unit-selection speech synthesis system using synthetic speech Naturalness evaluation score
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Heng Lu, Zhen-hua Ling, Ren-hua WangAbstract:This paper proposes a unit-selection and waveform concatenation speech synthesis system based on synthetic speech Naturalness evaluation. A Support Vector Machine (SVM) and Log Likelihood Ratio (LLR) based synthetic speech Naturalness evaluation system was introduced in our previous work. In this paper, the evaluation system is improved in three aspects. Finally, a unit-selection and concatenation waveform speech synthesis system is built on the base of the synthetic speech Naturalness evaluation system. Optimum unit sequence is chosen through the re-scoring for the N-best path. Subjective listening tests show the proposed synthetic speech evaluation based speech synthesis system significantly outperforms the traditional unit-selection speech synthesis system.
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ICASSP - Building HMM based unit-selection speech synthesis system using synthetic speech Naturalness evaluation score
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Heng Lu, Zhen-hua Ling, Ren-hua WangAbstract:This paper proposes a unit-selection and waveform concatenation speech synthesis system based on synthetic speech Naturalness evaluation. A Support Vector Machine (SVM) and Log Likelihood Ratio (LLR) based synthetic speech Naturalness evaluation system was introduced in our previous work. In this paper, the evaluation system is improved in three aspects. Finally, a unit-selection and concatenation waveform speech synthesis system is built on the base of the synthetic speech Naturalness evaluation system. Optimum unit sequence is chosen through the re-scoring for the N-best path. Subjective listening tests show the proposed synthetic speech evaluation based speech synthesis system significantly outperforms the traditional unit-selection speech synthesis system.
Heng Lu - One of the best experts on this subject based on the ideXlab platform.
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Building HMM based unit-selection speech synthesis system using synthetic speech Naturalness evaluation score
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Heng Lu, Zhen-hua Ling, Ren-hua WangAbstract:This paper proposes a unit-selection and waveform concatenation speech synthesis system based on synthetic speech Naturalness evaluation. A Support Vector Machine (SVM) and Log Likelihood Ratio (LLR) based synthetic speech Naturalness evaluation system was introduced in our previous work. In this paper, the evaluation system is improved in three aspects. Finally, a unit-selection and concatenation waveform speech synthesis system is built on the base of the synthetic speech Naturalness evaluation system. Optimum unit sequence is chosen through the re-scoring for the N-best path. Subjective listening tests show the proposed synthetic speech evaluation based speech synthesis system significantly outperforms the traditional unit-selection speech synthesis system.
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ICASSP - Building HMM based unit-selection speech synthesis system using synthetic speech Naturalness evaluation score
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Heng Lu, Zhen-hua Ling, Ren-hua WangAbstract:This paper proposes a unit-selection and waveform concatenation speech synthesis system based on synthetic speech Naturalness evaluation. A Support Vector Machine (SVM) and Log Likelihood Ratio (LLR) based synthetic speech Naturalness evaluation system was introduced in our previous work. In this paper, the evaluation system is improved in three aspects. Finally, a unit-selection and concatenation waveform speech synthesis system is built on the base of the synthetic speech Naturalness evaluation system. Optimum unit sequence is chosen through the re-scoring for the N-best path. Subjective listening tests show the proposed synthetic speech evaluation based speech synthesis system significantly outperforms the traditional unit-selection speech synthesis system.
Zhen-hua Ling - One of the best experts on this subject based on the ideXlab platform.
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Building HMM based unit-selection speech synthesis system using synthetic speech Naturalness evaluation score
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Heng Lu, Zhen-hua Ling, Ren-hua WangAbstract:This paper proposes a unit-selection and waveform concatenation speech synthesis system based on synthetic speech Naturalness evaluation. A Support Vector Machine (SVM) and Log Likelihood Ratio (LLR) based synthetic speech Naturalness evaluation system was introduced in our previous work. In this paper, the evaluation system is improved in three aspects. Finally, a unit-selection and concatenation waveform speech synthesis system is built on the base of the synthetic speech Naturalness evaluation system. Optimum unit sequence is chosen through the re-scoring for the N-best path. Subjective listening tests show the proposed synthetic speech evaluation based speech synthesis system significantly outperforms the traditional unit-selection speech synthesis system.
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ICASSP - Building HMM based unit-selection speech synthesis system using synthetic speech Naturalness evaluation score
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Heng Lu, Zhen-hua Ling, Ren-hua WangAbstract:This paper proposes a unit-selection and waveform concatenation speech synthesis system based on synthetic speech Naturalness evaluation. A Support Vector Machine (SVM) and Log Likelihood Ratio (LLR) based synthetic speech Naturalness evaluation system was introduced in our previous work. In this paper, the evaluation system is improved in three aspects. Finally, a unit-selection and concatenation waveform speech synthesis system is built on the base of the synthetic speech Naturalness evaluation system. Optimum unit sequence is chosen through the re-scoring for the N-best path. Subjective listening tests show the proposed synthetic speech evaluation based speech synthesis system significantly outperforms the traditional unit-selection speech synthesis system.
Amelie Rochetcapellan - One of the best experts on this subject based on the ideXlab platform.
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the transfer of learning as hci similarity towards an objective assessment of the sensory motor basis of Naturalness
Human Factors in Computing Systems, 2015Co-Authors: François Bérard, Amelie RochetcapellanAbstract:Human-computer interaction should be natural. However, the notion of natural is questioned due to a lack of theoretical background and methods to objectively measure the Naturalness of a HCI. A frequently cited aspect of natural HCIs is their ability to benefit from knowledge and skills that users develop in their interaction with the real (non-digital) world. Among these skills, sensory-motor abilities are essential to operate many HCIs. This suggests that the transfer of these abilities between physical and digital interactions could be used as an experimental tool to assess the sensory-motor similarity between interactions, and could be considered as an objective measurement of the sensory-motor grounding of Naturalness. In this framework, we introduce a new experimental paradigm inspired by motor learning research to assess sensory-motor similarity, as revealed by the transfer of learning. We tested this paradigm in an empirical study to question the Naturalness of three HCIs: direct-touch, mouse pointing and absolute indirect-touch. The study revealed how skill learning transfers from these three digital interactions towards an equivalent physical interaction. We observed strong transfer of skill between direct-touch and physical interaction, but no transfer from the other two interactions. This work provides a first objective assessment of the sensory-motor basis of direct-touch Naturalness, and a new empirical path to question HCI similarity and Naturalness.
François Bérard - One of the best experts on this subject based on the ideXlab platform.
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CHI - The Transfer of Learning as HCI Similarity: Towards an Objective Assessment of the Sensory-Motor Basis of Naturalness
Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI '15, 2015Co-Authors: François Bérard, Amélie Rochet-capellanAbstract:Human-computer interaction should be natural. However, the notion of natural is questioned due to a lack of theoretical background and methods to objectively measure the Naturalness of a HCI. A frequently cited aspect of natural HCIs is their ability to benefit from knowledge and skills that users develop in their interaction with the real (non-digital) world. Among these skills, sensory-motor abilities are essential to operate many HCIs. This suggests that the transfer of these abilities between physical and digital interactions could be used as an experimental tool to assess the sensory-motor similarity between interactions, and could be considered as an objective measurement of the sensory-motor grounding of Naturalness. In this framework, we introduce a new experimental paradigm inspired by motor learning research to assess sensory-motor similarity, as revealed by the transfer of learning. We tested this paradigm in an empirical study to question the Naturalness of three HCIs: direct-touch, mouse pointing and absolute indirect-touch. The study revealed how skill learning transfers from these three digital interactions towards an equivalent physical interaction. We observed strong transfer of skill between direct-touch and physical interaction, but no transfer from the other two interactions. This work provides a first objective assessment of the sensory-motor basis of direct-touch Naturalness, and a new empirical path to question HCI similarity and Naturalness.
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the transfer of learning as hci similarity towards an objective assessment of the sensory motor basis of Naturalness
Human Factors in Computing Systems, 2015Co-Authors: François Bérard, Amelie RochetcapellanAbstract:Human-computer interaction should be natural. However, the notion of natural is questioned due to a lack of theoretical background and methods to objectively measure the Naturalness of a HCI. A frequently cited aspect of natural HCIs is their ability to benefit from knowledge and skills that users develop in their interaction with the real (non-digital) world. Among these skills, sensory-motor abilities are essential to operate many HCIs. This suggests that the transfer of these abilities between physical and digital interactions could be used as an experimental tool to assess the sensory-motor similarity between interactions, and could be considered as an objective measurement of the sensory-motor grounding of Naturalness. In this framework, we introduce a new experimental paradigm inspired by motor learning research to assess sensory-motor similarity, as revealed by the transfer of learning. We tested this paradigm in an empirical study to question the Naturalness of three HCIs: direct-touch, mouse pointing and absolute indirect-touch. The study revealed how skill learning transfers from these three digital interactions towards an equivalent physical interaction. We observed strong transfer of skill between direct-touch and physical interaction, but no transfer from the other two interactions. This work provides a first objective assessment of the sensory-motor basis of direct-touch Naturalness, and a new empirical path to question HCI similarity and Naturalness.
