scholarly journals Phone based acoustic modeling for automatic speech recognition for Punjabi language

2021 ◽  
Vol 3 (1) ◽  
pp. 68-83
Author(s):  
Wiqas Ghai ◽  
Navdeep Singh
Keyword(s):  
Speech Recognition ◽  
Word Recognition ◽  
Automatic Speech Recognition ◽  
Recognition Accuracy ◽  
Model Based ◽  
The Media ◽  
Isolated Word ◽  
Whole Word ◽  
Speech Recognizer ◽  
Triphone Model

Punjabi language is a tonal language belonging to an Indo-Aryan language family and has a number of speakers all around the world. Punjabi language has gained acceptability in the media & communication and therefore deserves to have a place in the growing field of automatic speech recognition which has been explored already for a number of other Indian and foreign languages successfully. Some work has been done in the field of isolated word speech recognition for Punjabi language, but only using whole word based acoustic models. A phone based approach has yet to be applied for Punjabi language speech recognition. This paper describes an automatic speech recognizer that recognizes isolated word speech and connected word speech using a triphone based acoustic model on the HTK 3.4.1 speech Engine and compares the performance with acoustic whole word model based ASR system. Word recognition accuracy of isolated word speech was 92.05% for acoustic whole word model based system and 97.14% for acoustic triphone model based system whereas word recognition accuracy of connected word speech was 87.75% for acoustic whole word model based system and 91.62% for acoustic triphone model based system.

Noise Speech Recognition Based on Compressive Sensing

2011 ◽  
Vol 268-270 ◽  
pp. 82-87
Author(s):  
Zhi Peng Zhao ◽  
Yi Gang Cen ◽  
Xiao Fang Chen
Keyword(s):  
Speech Recognition ◽  
Word Recognition ◽  
Compressive Sensing ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Recognition System ◽  
Speech Recognition System ◽  
Recognition Method ◽  
Isolated Word ◽  
Isolated Word Recognition

In this paper, we proposed a new noise speech recognition method based on the compressive sensing theory. Through compressive sensing, our method increases the anti-noise ability of speech recognition system greatly, which leads to the improvement of the recognition accuracy. According to the experiments, our proposed method achieved better recognition performance compared with the traditional isolated word recognition method based on DTW algorithm.

Effects of Vocabulary and Implicit Linguistic Knowledge on Speech Recognition in Adverse Listening Conditions

2019 ◽  
Vol 28 (3S) ◽  
pp. 742-755 ◽  
Author(s):  
Annalise Fletcher ◽  
Megan McAuliffe ◽  
Sarah Kerr ◽  
Donal Sinex
Keyword(s):  
Working Memory ◽  
Speech Recognition ◽  
Word Recognition ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Vocabulary Knowledge ◽  
Nonverbal Intelligence ◽  
Signal To Noise ◽  
Signal Degradation ◽  
Hearing Acuity

Purpose This study aims to examine the combined influence of vocabulary knowledge and statistical properties of language on speech recognition in adverse listening conditions. Furthermore, it aims to determine whether any effects identified are more salient at particular levels of signal degradation. Method One hundred three young healthy listeners transcribed phrases presented at 4 different signal-to-noise ratios, which were coded for recognition accuracy. Participants also completed tests of hearing acuity, vocabulary knowledge, nonverbal intelligence, processing speed, and working memory. Results Vocabulary knowledge and working memory demonstrated independent effects on word recognition accuracy when controlling for hearing acuity, nonverbal intelligence, and processing speed. These effects were strongest at the same moderate level of signal degradation. Although listener variables were statistically significant, their effects were subtle in comparison to the influence of word frequency and phonological content. These language-based factors had large effects on word recognition at all signal-to-noise ratios. Discussion Language experience and working memory may have complementary effects on accurate word recognition. However, adequate glimpses of acoustic information appear necessary for speakers to leverage vocabulary knowledge when processing speech in adverse conditions.

Human Factors Guidelines for the use of Word Recognition Devices

1981 ◽  
Vol 25 (1) ◽  
pp. 710-710
Author(s):  
Thomas R. Edman
Keyword(s):  
Speech Recognition ◽  
Word Recognition ◽  
Human Factors ◽  
Data Processing ◽  
Recognition Accuracy ◽  
Affect Recognition ◽  
Sufficient Data ◽  
Noise Parameters ◽  
History Of ◽  
To Receive

Speech recognition devices are finding increasing use in industrial environments, and continue to receive interest for military and data processing applications. In the ten year commercial history of word recognition devices, a small but valuable body of literature has developed which can assist the human factors engineer in making best use of this technology. Similarly, an even larger, though less well structured body of informed experience with these devices has also been accumulated. Sufficient data therefore, exists to permit development of a preliminary set of human factors guidelines for the use of speech recognition devices. To be discussed will be: (1) procedures for training word recognition devices; (2) guidelines for vocabulary selection; (3) methods for evaluating the performance of word recognition devices; (4) criteria for selecting applications for word recognition. The emphasis will be on detailed factors which affect recognition accuracy in application environments, e.g., effects of noise, parameters of training and acoustic-phonetic aspects of vocabulary selection.

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