Deep learning for spoken language identification
نویسنده
چکیده
Empirical results have shown that many spoken language identification systems based on hand-coded features perform poorly on small speech samples where a human would be successful. A hypothesis for this low performance is that the set of extracted features is insufficient. A deep architecture that learns features automatically is implemented and evaluated on several datasets.
منابع مشابه
An Investigation of Spoken Output and Intervention Types among Iranian EFL Learners
This study was inspired by VanPatten and Uludag’s (2011) study on the transferability of training via processing instruction to output tasks and Mori’s (2002) work on the development of talk-in-interaction during a group task. An interview was devised as the pretest, posttest, and delayed posttest to compare four intervention types for teaching the simple past passive: traditional intervention ...
متن کاملThe Impact of Language Learning Activities on the Spoken Language Development of 5-6-Year-Old Children in Private Preschool Centers of Langroud
The Impact of Language Learning Activities on the Spoken Language Development of 5-6-Year-Old Children in Private Preschool Centers of Langroud N. Bagheri, M.A. E. Abbasi, Ph.D. M. GeramiPour, Ph.D. The present study was conducted to investigate the impact of language learning activities on development of spoken language in 5-6-year-old children at private preschool center...
متن کاملAdult’s Learning Strategies for Receptive Skill Self-managing or Teacher-managing
Receptive language skill refers to answering appropriately to another person's spoken language. A lot of teachers try to develop receptive language skills in their language learners. When receptive language skills are not appropriately acquired, learners may miss significant learning opportunities resulting in delays in the development and acquisition of spoken language. The goals of this paper...
متن کاملConditional Generative Adversarial Nets Classifier for Spoken Language Identification
The i-vector technique using deep neural network has been successfully applied in spoken language identification systems. Neural network modeling showed its effectiveness as both discriminant feature transformation and classification in many tasks, in particular with a large training data set. However, on a small data set, neural networks suffer from the overfitting problem which degrades the p...
متن کاملOn-Line Learning of a Persian Spoken Dialogue System Using Real Training Data
The first spoken dialogue system developed for the Persian language is introduced. This is a ticket reservation system with Persian ASR and NLU modules. The focus of the paper is on learning the dialogue management module. In this work, real on-line training data are used during the learning process. For on-line learning, the effect of the variations of discount factor (g) on the learning speed...
متن کامل