Titlebook: Speech and Computer; 18th International C Andrey Ronzhin,Rodmonga Potapova,Géza Németh Conference proceedings 2016 Springer International P

intoxicate

平淡而无味

A Phonetic Segmentation Procedure Based on Hidden Markov ModelsSeveral variants of the procedure are compared, and the usage of speaker-adapted context-dependent triphone models trained without the expanded manually checked data is proven to produce the best results. A few ways to improve the procedure even more, as well as future work, are also discussed.

Yourself

Graduated

我不明白

Advances in STC Russian Spontaneous Speech Recognition Systemacoustic model by the use of score fusion. The resulting system achieves WER of 16.4 %, with an absolute reduction of 8.7 % and relative reduction of 34.7 % compared to our previous system result on this test set.

猛烈责骂

Approaches for Out-of-Domain Adaptation to Improve Speaker Recognition PerformanceR systems effectiveness. This paper investigates discriminative and generative approaches for the adaptation of the parameters of the speaker recognition systems and proposes effective solutions to improve their performance.

Painstaking

Assessment of the Relation Between Low-Frequency Features and Velum Opening by Using Real Articulatoand velum movement is measured. In addition, the parameters are evaluated in an acoustic-to-articulatory system based on radial basis neural networks. Results suggest the existence of low-frequency features related to velum position. Therefore, this kind of parameters could be useful in acoustic-to-articulatory mapping systems.

悬挂

平常

0302-9743 016, held in Budapest, Hungary, in August 2016. The 85 papers presented in this volume were carefully reviewed and selected from 154 submissions..978-3-319-43957-0978-3-319-43958-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

CHIDE

A Preliminary Exploration of Group Social Engagement Level Recognition in Multiparty Casual Conversah topic about social group engagement in non-task oriented (casual) multiparty conversations. Fusion of hand-crafted acoustic and visual cues was used to predict social group engagement levels and was found to achieve higher results than using audio and visual cues separately.