scholarly journals Speech Rate Control for Improving Elderly Speech Recognition of Smart Devices

2017 ◽  
Vol 17 (2) ◽  
pp. 79-84 ◽  
Author(s):  
G. SON ◽  
S. KWON ◽  
Y. LIM
Keyword(s):  
Speech Recognition ◽  
Rate Control ◽  
Speech Rate ◽  
Smart Devices

Large-scale subjective evaluations of speech rate control methods for HMM-based speech synthesizers

2011 ◽  
Author(s):  
Tsuneo Kato ◽  
Makoto Yamada ◽  
Nobuyuki Nishizawa ◽  
Keiichiro Oura ◽  
Keiichi Tokuda
Keyword(s):  
Rate Control ◽  
Large Scale ◽  
Speech Rate ◽  
Control Methods ◽  
Subjective Evaluations

scholarly journals Speech Recognition of Oral English Teaching Based on Deep Belief Network

2020 ◽  
Vol 15 (10) ◽  
pp. 100
Author(s):  
Jianmei Wang
Keyword(s):  
Speech Recognition ◽  
Language Learning ◽  
Teaching And Learning ◽  
Teaching Method ◽  
Evaluation Model ◽  
Speech Rate ◽  
Pearson Correlation ◽  
Deep Belief Network ◽  
Belief Network ◽  
English Teaching

The oral English teaching faces several common problems: the teaching method is very inefficient, and the learners are poor in oral English. The development of computer-aided language learning offers a possible solution to these problems. Based on techniques of speech recognition, cloud computing and deep learning, this paper applies the deep belief network (DBN) to recognize the speeches in oral English teaching, and establishes a multi-parameter evaluation model for the pronunciation quality of oral English among college students. The model combines the merits of subjective and objective evaluations, and assesses the pronunciation from four aspects: pitch, speech rate, rhythm and intonation. Finally, the proposed model was verified through speech recognition and pronunciation evaluation experiments on 26 non-English majors from a college. The results show that the proposed evaluation model output credible results, which are consistent with those of experts, as evidenced by consistency, neighbourhood consistency and Pearson correlation coefficient. The research provides a feasible way to evaluate the oral English proficiency of learners, laying the basis for improving the teaching and learning efficiency of oral English.

Preprocessing for elderly speech recognition of smart devices

2016 ◽  
Vol 36 ◽  
pp. 110-121 ◽  
Author(s):  
Soonil Kwon ◽  
Sung-Jae Kim ◽  
Joon Yeon Choeh
Keyword(s):  
Speech Recognition ◽  
Smart Devices

Selected Cognitive Factors and Speech Recognition Performance Among Young and Elderly Listeners

1997 ◽  
Vol 40 (2) ◽  
pp. 423-431 ◽  
Author(s):  
Sandra Gordon-Salant ◽  
Peter J. Fitzgibbons
Keyword(s):  
Hearing Loss ◽  
Speech Recognition ◽  
Recognition Performance ◽  
Speech Rate ◽  
Normal Hearing ◽  
Recall Task ◽  
Cognitive Factors ◽  
Word Recall ◽  
Contextual Cues ◽  
Sentence Recall

The influence of selected cognitive factors on age-related changes in speech recognition was examined by measuring the effects of recall task, speech rate, and availability of contextual cues on recognition performance by young and elderly listeners. Stimuli were low and high context sentences from the R-SPIN test presented at normal and slowed speech rates in noise. Response modes were final word recall and sentence recall. The effects of hearing loss and age were examined by comparing performances of young and elderly listeners with normal hearing and young and elderly listeners with hearing loss. Listeners with hearing loss performed more poorly than listeners with normal hearing in nearly every condition. In addition, elderly listeners exhibited poorer performance than younger listeners on the sentence recall task, but not on the word recall task, indicating that added memory demands have a detrimental effect on elderly listeners' performance. Slowing of speech rate did not have a differential effect on performance of young and elderly listeners. All listeners performed well when stimulus contextual cues were available. Taken together, these results support the notion that the performance of elderly listeners with hearing loss is influenced by a combination of auditory processing factors, memory demands, and speech contextual information.

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