scholarly journals New report preparation system for endoscopic procedures using speech recognition technology

2018 ◽  
Vol 06 (06) ◽  
pp. E676-E687 ◽  
Author(s):  
Toshitatsu Takao ◽  
Ryo Masumura ◽  
Sumitaka Sakauchi ◽  
Yoshiko Ohara ◽  
Elif Bilgic ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Reporting System ◽  
Recognition Rate ◽  
Data Entry ◽  
Recognition System ◽  
Endoscopic Procedures ◽  
Group A ◽  
The Mean ◽  
The Times

Abstract Background and study aims We developed a new reporting system based on structured data entry, which selectively extracts only endoscopic findings from endoscopists’ oral statements and automatically inputs them into appropriate columns in real time during endoscopic procedures. Methods We compared the time for endoscopic procedures and report preparation (ER time) by using an esophagogastroduodenoscopy simulator in three groups: one preparing reports using a mouse after endoscopic procedures (CE group); a second group preparing reports by using voice alone during endoscopic procedures (SR group); and the final group preparing reports by operating the system with a foot switch and inputting findings using voice during endoscopic procedures (SR + FS group). For the SR and SR + FS groups, we identified the recognition rates of the speech recognition system. Results Mean ER times for cases with three findings each were 162, 130 and 119 seconds in the CE, SR and SR + FS groups, respectively. The mean ER times for cases with six findings each were 220, 144 and 128 seconds, respectively. The times in the SR and SR + FS groups were significantly shorter than that in the CE group (P < 0.017). The recognition rate of the SR group for cases with three findings each was 98.4 %, and 97.6 % in the same group for cases with six findings each. The rates in the SR + FS group were 95.2 % and 98.4 %, respectively. Conclusion Our reporting system was demonstrated to allow an endoscopist to efficiently complete the report in real time during endoscopic procedures.

scholarly journals Enhanced Automatic Speech Recognition System Based on Enhancing Power-Normalized Cepstral Coefficients

2019 ◽  
Vol 9 (10) ◽  
pp. 2166 ◽  
Author(s):  
Mohamed Tamazin ◽  
Ahmed Gouda ◽  
Mohamed Khedr
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Additive White Gaussian Noise ◽  
Recognition Rate ◽  
Data Entry ◽  
Recognition System ◽  
Speech Recognition System ◽  
Automatic Speech Recognition System ◽  
Different Types ◽  
A New Technique

Many new consumer applications are based on the use of automatic speech recognition (ASR) systems, such as voice command interfaces, speech-to-text applications, and data entry processes. Although ASR systems have remarkably improved in recent decades, the speech recognition system performance still significantly degrades in the presence of noisy environments. Developing a robust ASR system that can work in real-world noise and other acoustic distorting conditions is an attractive research topic. Many advanced algorithms have been developed in the literature to deal with this problem; most of these algorithms are based on modeling the behavior of the human auditory system with perceived noisy speech. In this research, the power-normalized cepstral coefficient (PNCC) system is modified to increase robustness against the different types of environmental noises, where a new technique based on gammatone channel filtering combined with channel bias minimization is used to suppress the noise effects. The TIDIGITS database is utilized to evaluate the performance of the proposed system in comparison to the state-of-the-art techniques in the presence of additive white Gaussian noise (AWGN) and seven different types of environmental noises. In this research, one word is recognized from a set containing 11 possibilities only. The experimental results showed that the proposed method provides significant improvements in the recognition accuracy at low signal to noise ratios (SNR). In the case of subway noise at SNR = 5 dB, the proposed method outperforms the mel-frequency cepstral coefficient (MFCC) and relative spectral (RASTA)–perceptual linear predictive (PLP) methods by 55% and 47%, respectively. Moreover, the recognition rate of the proposed method is higher than the gammatone frequency cepstral coefficient (GFCC) and PNCC methods in the case of car noise. It is enhanced by 40% in comparison to the GFCC method at SNR 0dB, while it is improved by 20% in comparison to the PNCC method at SNR −5dB.

scholarly journals Design and Implementation of Embedded Real-Time English Speech Recognition System Based on Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lifang He ◽  
Gaimin Jin ◽  
Sang-Bing Tsai
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Optimal Parameter ◽  
Design Method ◽  
Fitness Function ◽  
Recognition Rate ◽  
Hardware Acceleration ◽  
Recognition System ◽  
Speech Recognition System ◽  
The Real

This article uses Field Programmable Gate Array (FPGA) as a carrier and uses IP core to form a System on Programmable Chip (SOPC) English speech recognition system. The SOPC system uses a modular hardware system design method. Except for the independent development of the hardware acceleration module and its control module, the other modules are implemented by software or IP provided by Xilinx development tools. Hardware acceleration IP adopts a top-down design method, provides parallel operation of multiple operation components, and uses pipeline technology, which speeds up data operation, so that only one operation cycle is required to obtain an operation result. In terms of recognition algorithm, a more effective training algorithm is proposed, Genetic Continuous Hidden Markov Model (GA_CHMM), which uses genetic algorithm to directly train CHMM model. It is to find the optimal model by encoding the parameter values of the CHMM and performing operations such as selection, crossover, and mutation according to the fitness function. The optimal parameter value after decoding corresponds to the CHMM model, and then the English speech recognition is performed through the CHMM algorithm. This algorithm can save a lot of training time, thereby improving the recognition rate and speed. This paper studies the optimization of embedded system software. By studying the fixed-point software algorithm and the optimization of system storage space, the real-time response speed of the system has been reduced from about 10 seconds to an average of 220 milliseconds. Through the optimization of the CHMM algorithm, the real-time performance of the system is improved again, and the average time to complete the recognition is significantly shortened. At the same time, the system can achieve a recognition rate of over 90% when the English speech vocabulary is less than 200.

scholarly journals A Real Time Noise-Robust Speech Recognition System

1970 ◽  
Vol 1 (2) ◽  
pp. 75-83
Author(s):  
Naoya Wada ◽  
Shingo Yoshizawa ◽  
Yoshikazu Miyanaga
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Dynamic Range ◽  
Recognition Rate ◽  
Recognition System ◽  
Noise Robustness ◽  
Fpga Design ◽  
Male And Female ◽  
Parallel Pipelined ◽  
Phase Recognition

This paper introduces the extraction of speech features realizing noise robustness for speech recognition. It also explores advanced speech analysis techniques named RSF (Running Spectrum Filtering)/DRA (Dynamic Range Adjustment) in detail. The new experiments on phase recognition were carried out using 40 male and female speakers for training and 5 other male and female speakers for recognition. The result of recognition rate is improved from 17% to 63% under car noise at -10dB SNR for example. It shows the high noise robustness of the proposed system. In addition, the new parallel/pipelined LSI design of the system is proposed. It considerably reduces the calculation time. Using this architecture, the real time speech recognition can be developed. For this system, both of full-custom LSI design and FPGA design are introduced.

scholarly journals Enhancement of Speech Recognition System by neural network approaches of Clustering

2013 ◽  
Vol 6 (1) ◽  
pp. 266-271
Author(s):  
Anurag Upadhyay ◽  
Chitranjanjit Kaur
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Recurrent Neural Networks ◽  
Alternative Energy ◽  
Recognition Rate ◽  
Speech Sound ◽  
Recognition System ◽  
Training Methods ◽  
Indian Languages ◽  
Phone Recognition

This paper addresses the problem of speech recognition to identify various modes of speech data. Speaker sounds are the acoustic sounds of speech. Statistical models of speech have been widely used for speech recognition under neural networks. In paper we propose and try to justify a new model in which speech co articulation the effect of phonetic context on speech sound is modeled explicitly under a statistical framework. We study speech phone recognition by recurrent neural networks and SOUL Neural Networks. A general framework for recurrent neural networks and considerations for network training are discussed in detail. SOUL NN clustering the large vocabulary that compresses huge data sets of speech. This project also different Indian languages utter by different speakers in different modes such as aggressive, happy, sad, and angry. Many alternative energy measures and training methods are proposed and implemented. A speaker independent phone recognition rate of 82% with 25% frame error rate has been achieved on the neural data base. Neural speech recognition experiments on the NTIMIT database result in a phone recognition rate of 68% correct. The research results in this thesis are competitive with the best results reported in the literature. 

scholarly journals Voice Controlled Vehicle Dashboard

2020 ◽  
Vol 9 (1) ◽  
pp. 1022-1027
Keyword(s):  
Speech Recognition ◽  
Speaker Recognition ◽  
Feature Matching ◽  
Heavy Traffic ◽  
Recognition Rate ◽  
Recognition System ◽  
Low Noise ◽  
Raspberry Pi ◽  
Mel Frequency Cepstral Coefficients ◽  
Power Spectral

Driving a vehicle or a car has become tedious job nowadays due to heavy traffic so focus on driving is utmost important. This makes a scope for automation in Automobiles in minimizing human intervention in controlling the dashboard functions such as Headlamps, Indicators, Power window, Wiper System, and to make it possible this is a small effort from this paper to make driving distraction free using Voice controlled dashboard. and system proposed in this paper works on speech commands from the user (Driver or Passenger). As Speech Recognition system acts Human machine Interface (HMI) in this system hence this system makes use of Speaker recognition and Speech recognition for recognizing the command and recognize whether the command is coming from authenticated user(Driver or Passenger). System performs Feature Extraction and extracts speech features such Mel Frequency Cepstral Coefficients(MFCC),Power Spectral Density(PSD),Pitch, Spectrogram. Then further for Feature matching system uses Vector Quantization Linde Buzo Gray(VQLBG) algorithm. This algorithm makes use of Euclidean distance for calculating the distance between test feature and codebook feature. Then based on speech command recognized controller (Raspberry Pi-3b) activates the device driver for motor, Solenoid valve depending on function. This system is mainly aimed to work in low noise environment as most speech recognition systems suffer when noise is introduced. When it comes to speech recognition acoustics of the room matters a lot as recognition rate differs depending on acoustics. when several testing and simulation trials were taken for testing, system has speech recognition rate of 76.13%. This system encourages Automation of vehicle dashboard and hence making driving Distraction Free.

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