• The Journal of the Acoustical Society of Korea
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• v.35 no.1
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• pp.74-82
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• 2016

In this paper, a new automatic speech recognition (ASR) was proposed where ultrasonic doppler signals were used, instead of conventional speech signals. The proposed method has the advantages over the conventional speech/non-speech-based ASR including robustness against acoustic noises and user comfortability associated with usage of the non-contact sensor. In the method proposed herein, 40 kHz ultrasonic signal was radiated toward to the mouth and the reflected ultrasonic signals were then received. Frequency shift caused by the doppler effects was used to implement ASR. The proposed method employed multi-channel ultrasonic signals acquired from the various locations, which is different from the previous method where single channel ultrasonic signal was employed. The PCA(Principal Component Analysis) coefficients were used as the features of ASR in which hidden markov model (HMM) with left-right model was adopted. To verify the feasibility of the proposed ASR, the speech recognition experiment was carried out the 60 Korean isolated words obtained from the six speakers. Moreover, the experiment results showed that the overall word recognition rates were comparable with the conventional speech-based ASR methods and the performance of the proposed method was superior to the conventional signal channel ASR method. Especially, the average recognition rate of 90 % was maintained under the noise environments.

• Phonetics and Speech Sciences
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• v.6 no.2
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• pp.9-20
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• 2014

In this paper, we propose an automatic fluency evaluation algorithm for English speaking tests. In the proposed algorithm, acoustic features are extracted from an input spoken utterance and then fluency score is computed by using support vector regression (SVR). We estimate the parameters of feature modeling and SVR using the speech signals and the corresponding scores by human raters. From the correlation analysis results, it is shown that speech rate, articulation rate, and mean length of runs are best for fluency evaluation. Experimental results show that the correlation between the human score and the SVR score is 0.87 for 3 speaking tests, which suggests the possibility of the proposed algorithm as a secondary fluency evaluation tool.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1135-1148
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• 2017

Speech recognition is one of the fascinating fields in the area of Computer science. Accuracy of speech recognition system may reduce due to the presence of noise present in speech signal. Therefore noise removal is an essential step in Automatic Speech Recognition (ASR) system and this paper proposes a new technique called combined thresholding for noise removal. Feature extraction is process of converting acoustic signal into most valuable set of parameters. This paper also concentrates on improving Mel Frequency Cepstral Coefficients (MFCC) features by introducing Discrete Wavelet Packet Transform (DWPT) in the place of Discrete Fourier Transformation (DFT) block to provide an efficient signal analysis. The feature vector is varied in size, for choosing the correct length of feature vector Self Organizing Map (SOM) is used. As a single classifier does not provide enough accuracy, so this research proposes an Ensemble Support Vector Machine (ESVM) classifier where the fixed length feature vector from SOM is given as input, termed as ESVM_SOM. The experimental results showed that the proposed methods provide better results than the existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.595-602
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• 2021

Marine accidents are mostly caused by medium and small ships, and are continuously increasing. In this paper, we propose an architecture of the speech synthesis based automatic safety navigation support service providing system for small ships that equiped onboard systems compared with vessels. The main purpose of the system is to prevent marine accidents by providing synthesized voice safety messages to nearby ships. The safety navigation support service is operated by connecting GPS and AIS to synthesize voice safety messages, automatically broadcast through VHF. Therefore, we developed a data processing module, a staged risk analysis module, a voice synthesis safety message generation module, and a VHF broadcasting equipment control module, which are components of the system. In addition, we conducted laboratory-level and sea-trial demonstration tests using the developed the system, which verified usefulness of the proposed service.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.9 no.2
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• pp.142-146
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• 1998

Background : It has been established that the fundamental frequency(Fo) of the vowels varies systemically as a function of vowel height. Specifically, high vowels have a higher Fo than low vowels. Two major explanations or hypotheses dominate contemporary accounts of fired to explain the mechanisms underlying intrinsic variation in vowel Fo, source-tract coupling hypothesis and tongue-pull hypothesis. Objectives : Total laryngectomy surgery necessiates removal of all structures between the hyoid bone and the tracheal rings. Therefore, the assumption that no direct interconnection exists between the tongue and pharyngoesophageal segment that would mediate systematic variation in vowel Fo appears quite reasonable. If tongue-pull hypothesis is correct, systemic differences in Fo between high versus low vowels produced by esophageal speakers would not Or expected. We analyzed the Fo in the vowels of esophageal voice. Materials and method : The subjects were 11 cases of laryngectomee patients with fluent esophageal voice. The five essential vowels were recorded and analyzed with computer speech analysis system(Computerized Speech Lab). The Fo was measured using acoustic waveform, automatically and manually, and narrow band spectral analysis. Results : The results of this study reveal that intrinsic variation in vowel Fo is clearly evident in esophageal speech. By analysis using acoustic waveform automatically, the signals were too irregular to measure the Fo precisely. So the data from automatic analysis of acoustic waveform is not logical. But the Fo by measuring with manually calculated acoustic waveform or narrowband spectral analysis resulted in acceptable results. These results were interpreted to support neither the source-tract coupling nor the tongue-pull hypotheses and led us to offer an alternative explanation to account for intrinsic variation of Fo.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.494-500
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• 2008

In the field of development of human interface technology, the interactions between human and machine are important. The research on emotion recognition helps these interactions. This paper presents an algorithm for emotion recognition based on personalized speech signals. The proposed approach is trying to extract the characteristic of speech signal for emotion recognition using PLP (perceptual linear prediction) analysis. The PLP analysis technique was originally designed to suppress speaker dependent components in features used for automatic speech recognition, but later experiments demonstrated the efficiency of their use for speaker recognition tasks. So this paper proposed an algorithm that can easily evaluate the personal emotion from speech signals in real time using personalized emotion patterns that are made by PLP analysis. The experimental results show that the maximum recognition rate for the speaker dependant system is above 90%, whereas the average recognition rate is 75%. The proposed system has a simple structure and but efficient to be used in real time.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.386-391
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• 2008

Some spoken word errors that violate grammatical or writing rules occurs frequently in communication environments like mobile phone and messenger. These unexpected errors cause a problem in a language processing system for many applications like speech recognition, text-to-speech translation, and so on. In this paper, we proposed and implemented an automatic correction system of ill-formed words and word spacing errors in SMS sentences that has been the major errors of poor accuracy. We experimented three methods of constructing the word correction dictionary and evaluated the results of those methods. They are (1) manual construction of error words from the vocabulary list of ill-formed communication languages, (2) automatic construction of error dictionary from the manually constructed corpus, and (3) context-dependent method of automatic construction of error dictionary.

• The Journal of the Acoustical Society of Korea
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• v.19 no.3
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• pp.51-56
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• 2000

Is this paper, we studied on the automatic speech control system in real-time windows environment using voice recognition. The applied reference pattern is the variable DMS model which is proposed to fasten execution speed and the one-stage DP algorithm using this model is used for recognition algorithm. The recognition vocabulary set is composed of control command words which are frequently used in windows environment. In this paper, an automatic speech period detection algorithm which is for on-line voice processing in windows environment is implemented. The variable DMS model which applies variable number of section in consideration of duration of the input signal is proposed. Sometimes, unnecessary recognition target word are generated. therefore model is reconstructed in on-line to handle this efficiently. The Perceptual Linear Predictive analysis method which generate feature vector from extracted feature of voice is applied. According to the experiment result, but recognition speech is fastened in the proposed model because of small loud of calculation. The multi-speaker-independent recognition rate and the multi-speaker-dependent recognition rate is 99.08% and 99.39% respectively. In the noisy environment the recognition rate is 96.25%.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.5
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• pp.71-81
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• 1997

This paper proposes noun and affix extraction methods using conjunctive information for making an automatic indexing system thorugh morphological analysis and syntactic analysis. The korean language has a peculiar spacing words rule, which is different from other languages, and the conjunctive information, which is extracted from the rule, can reduce the number of multiple parts of speech at a minimum cost. The proposed algorithms also solve the problem that one word is seperated by newline charcter. We show efficiency of the proposed algorithms through the process of morhologica analyzing.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.3
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• pp.215-221
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• 2016

This paper describes an automatic vowel sequence reproduction system for a talking robot built to reproduce the human voice based on the working behavior of the human articulatory system. A sound analysis system is developed to record a sentence spoken by a human (mainly vowel sequences in the Japanese language) and to then analyze that sentence to give the correct command packet so the talking robot can repeat it. An algorithm based on a short-time energy method is developed to separate and count sound phonemes. A matching template using partial correlation coefficients (PARCOR) is applied to detect a voice in the talking robot's database similar to the spoken voice. Combining the sound separation and counting the result with the detection of vowels in human speech, the talking robot can reproduce a vowel sequence similar to the one spoken by the human. Two tests to verify the working behavior of the robot are performed. The results of the tests indicate that the robot can repeat a sequence of vowels spoken by a human with an average success rate of more than 60%.