A Research on HMM based Speech Recognition in Spoken English

2021 ◽  
Vol 14 ◽  
Author(s):  
Na Wang ◽  
Xiaohong Zhang ◽  
Ashutosh Sharma
Keyword(s):  
Speech Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Learning System ◽  
Computer Assisted ◽  
Speech Signals ◽  
English Learning ◽  
Speech Recognition System ◽  
Spoken English ◽  
High Recognition Accuracy

: The computer assisted speech recognition system enabling voice recognition for understanding the spoken words using sound digitization is extensively being used in the field of education, scientific research, industry, etc. This article unveils the technological perspective of automated speech recognition system in order to realize the spoken English speech recognition system based on MATLAB. A speech recognition technology has been designed and implemented in this work which can collect the speech signals of the spoken English learning system and then filter those speech signals. This paper mainly adopts the preprocessing module for the processing of the raw speech data collected utilizing the MATLAB commands. The method of feature extraction is based on HMM model, codebook generation and template training. The research results show that the recognition accuracy of 98% is achieved by the spoken English speech recognition system studied in this paper. It can be seen that the spoken English speech recognition system based on MATLAB has high recognition accuracy and fast speed. This work addresses the current research issued needed to be tackled in the speech recognition field. This approach is able to provide the technical support and interface for the spoken English learning system.

scholarly journals Continuous Speech Recognition System for Kannada Language with Triphone Modelling using HTK

2019 ◽  
Vol 8 (3) ◽  
pp. 7827-7831
Keyword(s):  
Speech Recognition ◽  
Recognition Accuracy ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Experimental Result ◽  
Speech Recognition System ◽  
Continuous Speech Recognition ◽  
Frequency Scale ◽  
Context Dependent ◽  
Mel Frequency Cepstral Coefficient

Kannada is the regional language of India spoken in Karnataka. This paper presents development of continuous kannada speech recognition system using monophone modelling and triphone modelling using HTK. Mel Frequency Cepstral Coefficient (MFCC) is used as feature extractor, exploits cepstral and perceptual frequency scale leads good recognition accuracy. Hidden Markov Model is used as classifier. In this paper Gaussian mixture splitting is done that captures the variations of the phones. The paper presents performance of continuous Kannada Automatic Speech Recognition (ASR) system with respect to 2, 4,8,16 and 32 Gaussian mixtures with monophone and context dependent tri-phone modelling. The experimental result shows that good recognition accuracy is achieved for context dependent tri-phone modelling than monophone modelling as the number Gaussian mixture is increased.

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.

Implementation of Embedded Unspecific Continuous English Speech Recognition Based on HMM

2021 ◽  
Vol 14 ◽  
Author(s):  
Xiaoli Lu ◽  
Mohd Asif Shah
Keyword(s):  
Speech Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Vital Role ◽  
Recognition Algorithm ◽  
Theoretical Research ◽  
Fast Speed ◽  
Conversational Interfaces ◽  
Recognition Function ◽  
High Recognition Accuracy

Background: Human-computer interaction plays a vital role through Natural Language Conversational Interfaces to improve the usage of computers. Speech recognition technology allows the machine to understand human language. A speech recognition algorithm is used to achieve this function. Methodology: This paper is mainly based on the fundamental theoretical research of speech signals, establishes the HMM model, uses speech collection, recognition, and other methods, simulates on MATLAB, and integrates the recognition system ported to ARM for debugging and running to realize the embedded speech recognition function based on HMM under the ARM platform. Conclusion: The conclusion shows that the HMM-based embedded unspecific continuous English speech recognition system has high recognition accuracy and fast speed.

Keyword Recognition Based on MFCC

2014 ◽  
Vol 926-930 ◽  
pp. 1729-1732
Author(s):  
Sha Yang ◽  
Tian Hu ◽  
Yun Lu Zhang
Keyword(s):  
Speech Recognition ◽  
Recognition Accuracy ◽  
Hidden Markov ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Speech Recognition System ◽  
Continuous Speech Recognition ◽  
Recognition Time ◽  
State 1 ◽  
Model Approach

After about 50 years of development, speech recognition technology has been able to achieve large vocabulary, non-specific human continuous speech recognition system. On account of Chinese pronunciation features, we research the small vocabulary, non-specific Chinese speech recognition based on continuous Hidden Markov Model approach. With comparing the datasets of VQ/DTW, VQ/DHMM, CHMM state-1 recognition algorithm and CHMM state-2 recognition algorithm, the results of our experiment show that: (1) CHMM state-2 branch method performs primely in reduction of the recognition time; and (2) the recognition accuracy is improved eventually.

scholarly journals Using Vector Quantization Technique in Recognition of Gurbani Hymns (Japji Sahib): LBG Algorithm (VQ)

2019 ◽  
Vol 8 (6) ◽  
pp. 4708-4713
Keyword(s):  
Speech Recognition ◽  
Vector Quantization ◽  
Automatic Speech Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Background Music ◽  
Speech Recognition System ◽  
Continuous Mode ◽  
Speech Corpus ◽  
Lbg Algorithm

An improved and different variation of Automatic Speech Recognition (ASR) is presented which is based on Vector Quantization (VQ). ASR for different languages and different applications has been introduced so far. In this paper, we have presented a Speech Recognition system to recognize the hymns (paath) of Gurbani (sentences of Japji Sahib) as continuous mode of speech. For this, speech corpus has been generated in which the entire path has been recited by different speakers. The speech mode here can be taken as continuous speech encapsulated with background music and different kinds of additional noises and have been eliminated. The work has been done by using VQ approach of speech recognition and LBG algorithm which design optimal codebooks for the process of recognition. Experimental results are included which show that recognition accuracy for such system was found to be 92.6% and 95.8% for different and same speakers with different and same sentences.

Effect of Noise on Automatic Speech Recognition System Error Rate

2000 ◽  
Vol 44 (37) ◽  
pp. 606-609
Author(s):  
Fang Chen ◽  
Cristiano Masi
Keyword(s):  
Speech Recognition ◽  
Task Performance ◽  
Automatic Speech Recognition ◽  
Recognition Accuracy ◽  
Mental Workload ◽  
Recognition System ◽  
Physical Workload ◽  
Speech Recognition System ◽  
Automatic Speech Recognition System ◽  
Vocal Quality

Many studies have indicted that stress and workload can effect the recognition accuracy of the speech recognition system. This can include noise, vibration, G-force, information overload, vocal quality in noise, vocal quality and psychological stress, concurrent task performance and vocal fatigue. The commercially available speech recognition system has not yet reached the perfect design to recognize natural human speech. The military application of automatic speech recognition systems has been studied in a wide arrangement. Verbex’ Voice Master was recommended in its instruction book as especially suited well for use in a noisy environment. This system was selected as a candidate system for use in cockpits. Before implementing it in the cockpit, its strengths and weaknesses for special utterances need to be tested in a laboratory environment. The purpose of the study was to investigate the effects of noise on recognition accuracy in dual-task performance. The experiment was carried out in a noise-insulated room. The Verbex’ Voice Master speech recognition system was installed into the computer. Eleven male Swedish students were the subjects. Two noise levels were set up with a combination of mental workload and physical workload. The results showed that without noise and mental workload, the recognition accuracy could be as good as 99.4%. With noise and mental workload, the recognition accuracy could be reduced to 95%. The results indicated that noise had significant effects on the computer error while mental workload had significant effects on both subject error and computer error.

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