• Journal of Korea Multimedia Society
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• v.13 no.9
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• pp.1322-1328
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• 2010

Recently, keyword spotting system is greatly in the limelight as UI(User Interface) technology of ubiquitous home network system. Keyword spotting system is vulnerable to non-stationary noises such as TV, radio, dialogue. Especially, speech recognition rate goes down drastically under the embedded DSP(Digital Signal Processor) environments because it is relatively low in the computational capability to process input speech in real-time. In this paper, we propose a new keyword spotting system using the call-command method, which is consisted of small number of recognition networks. We select the call-command such as 'narae', 'home manager' and compose the small network as a token which is consisted of silence with the noise and call commands to carry the real-time recognition continuously for input speeches.

• The Journal of the Acoustical Society of Korea
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• v.31 no.1
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• pp.29-38
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• 2012

This paper presents a transient noise reduction system in a speech signal. The proposed transient noise reduction system utilizes a median filter to reduce the transient noise. Since the median filter can distort speech during the noise reduction, a long-term prediction (LTP) filter is adopted as a pre-processor to minimize speech distortion. The speech information preserved by the LTP filter is re-synthesized after reducing the noise. This paper verifies the weakness of a linear prediction (LP) filter and the superiority of the LTP filter for preserving the speech component in transient noise presence environment. Applying the proposed system, the signal-to-noise ratio (SNR) of output is improved by 8dB in both speech and noise presence region, and PESQ score is increased by 1 point comparing with noisy input.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7C
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• pp.962-970
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• 2004

This paper presents an efficient implementation of the 2.4 kbps EHSX(Enhanced Harmonic Stochastic Excitation) speech coder on a TMS320C6701$^{TM}$ floating-point digital signal processor. The EHSX speech codec is based on a harmonic and CELP(Code Excited Linear Prediction) modeling of the excitation signal respectively according to the frame characteristic such as a voiced speech and an unvoiced speech. In this paper, we represent the optimization methods to reduce the complexity for real-time implementation. The complexity in the filtering of a CELP algorithm that is the main part for the EHSX algorithm complexity can be reduced by converting program using floating-point variable to program using fixed-point variable. We also present the efficient optimization methods including the code allocation considering a DSP architecture and the low complexity algorithm of harmonic/pitch search in encoder part. Finally, we obtained the subjective quality of MOS 3.28 from speech quality test using the PESQ(perceptual evaluation of speech quality), ITU-T Recommendation P.862 and could get a goal of realtime operation of the EHSX codec.c.

• The Journal of the Acoustical Society of Korea
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• v.19 no.4
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• pp.45-51
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• 2000

This paper describes real-time implementation of a multi-channel G.729A speech coder using a 16 bit fixed-point Digital Signal Processor (DSP) and its application to a Voice Mailing Service (VMS) system. TMS320C549 by Texas Instruments was used as a fixed point DSP chip and a 4 channel G.729A coder was implemented on the chip. The implemented coder required 14.5 MIPS for the encoder and 3.6 MIPS for the decoder at each channel. In addition, memories required by the coder were 9.88K words and 1.69K words for code and data sections, respectively. As a result, the developed VMS system that accommodates two DSP chips was able to support totally 8 channels. Experimental results showed that the our multi-channel coder passes all of test vectors provided by ITU-T.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2
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• pp.50-61
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• 1995

A time scale modification method yielding rate-modified speech while conserving the characteristic of speech was implemented in real-time using a goneral purpose digital signal processor. Time scale modification changed pronunciation speed only, producing a time difference between the input signal and the modified signal, making it impossible to implement it in real-time. In this thesis, a system was implemented to remove the time difference between the input and modified signals. Speech signals slowed down or speeded up by a physical time scale modification method, such as adjusting the motor speed of the cassett tape recorder, was used as the input signal. Physical modification that controled only the inter speed of the cassette tape player distorted the pitch period of the original speech. In this study, a real-time system was implemented so that the pitch-distorted speech was reconstructed back to the original by fractional sampling pitch shifting using an FIR filter, and this signal was time scale modified to match the cassette tape recorder motor speed using SOLA time-scale medification. In experiments using speech signals medifiedby the proposed method, results obtained using a 16-bit resolution ADSP2101 processor and using computer simulations employing floating point operations showed about the same average frame signal-to-noise ratio of about 20 dB.

• Korean Journal of Cognitive Science
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• v.22 no.3
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• pp.327-345
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• 2011

In this paper, a new approach to Korean POS (Part-of-Speech) tagging is proposed. In previous works, a Korean POS tagger was regarded as a post-processor of a morphological analyzer, and as such a tagger was used to determine the most likely morpheme/POS sequence from morphological analysis. In the proposed approach, however, the POS tagger is supposed to generate the most likely morpheme and POS pair sequence directly from the given sentences. 398,632 eojeol POS-tagged corpus and 33,467 eojeol test data are used for training and evaluation, respectively. The proposed approach shows 96.31% of POS tagging accuracy.

• Journal of information and communication convergence engineering
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• v.13 no.3
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• pp.145-151
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• 2015

We describe in this paper a hardware-based improvement scheme of a real-time automatic speech recognition (ASR) system with respect to speed by designing a parallel feature extraction algorithm on a Field-Programmable Gate Array (FPGA). A computationally intensive block in the algorithm is identified implemented in hardware logic on the FPGA. One such block is mel-frequency cepstrum coefficient (MFCC) algorithm used for feature extraction process. We demonstrate that the FPGA platform may perform efficient feature extraction computation in the speech recognition system as compared to the generalpurpose CPU including the ARM processor. The Xilinx Zynq-7000 System on Chip (SoC) platform is used for the MFCC implementation. From this implementation described in this paper, we confirmed that the FPGA platform is approximately 500× faster than a sequential CPU implementation and 60× faster than a sequential ARM implementation. We thus verified that a parallelized and optimized MFCC architecture on the FPGA platform may significantly improve the execution time of an ASR system, compared to the CPU and ARM platforms.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.293-300
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• 2016

The Voice recognition is one of convenient methods to communicate between human and robots. This study proposes a speech recognition method using speech recognizers based on Hidden Markov Model (HMM) with a combination of techniques to enhance a biped robot control. In the past, Artificial Neural Networks (ANN) and Dynamic Time Wrapping (DTW) were used, however, currently they are less commonly applied to speech recognition systems. This Research confirms that the HMM, an accepted high-performance technique, can be successfully employed to model speech signals. High recognition accuracy can be obtained by using HMMs. Apart from speech modeling techniques, multiple feature extraction methods have been studied to find speech stresses caused by emotions and the environment to improve speech recognition rates. The procedure consisted of 2 parts: one is recognizing robot commands using multiple HMM recognizers, and the other is sending recognized commands to control a robot. In this paper, a practical voice recognition system which can recognize a lot of task commands is proposed. The proposed system consists of a general purpose microprocessor and a useful voice recognition processor which can recognize a limited number of voice patterns. By simulation and experiment, it was illustrated the reliability of voice recognition rates for application of the manufacturing process.

• The Journal of the Acoustical Society of Korea
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• v.19 no.2
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• pp.101-105
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• 2000

A telegram has been used to transmit the emergency news or celebration message. So, it has been very important media in our life. Although the telegram processing is more and more convenient, on the other hand, the telegram service contains only text message. The voice telegram is that delivering user's voice with text message. So, the voice telegram can be delivered sender's emotions and feelings. However, since voice information contains lots of data, large memory size and high cost processor are needed to deliver itself. In this paper, we proposed a new speech waveform coding method that has low complexity and low cost implementation for the voice telegram system. First, we fixed one basic speech waveform per pitch period and measured the waveform similarity between basic and neighbor speech waveform. Second, if the similarity satisfied threshold values, we compress the neighbor speech waveform with pitch and magnitude value per pitch period and if not, we save speech waveform. When the compression is about 45%, we obtained about 4 point in MOS.

• The Journal of the Acoustical Society of Korea
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• v.19 no.4
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• pp.31-36
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• 2000

This paper describes the implementation and the evaluation of the speech recognition automatic exchange system. The system provides government or public offices, companies, educational institutions that are composed of large number of members and parts with exchange service using speech recognition technology. The recognizer of the system is a Speaker-Independent, Isolated-word, Flexible-Vocabulary recognizer based on SCHMM(Semi-Continuous Hidden Markov Model). For real-time implementation, DSP TMS320C32 made in Texas Instrument Inc. is used. The system operating terminal including the diagnosis of speech recognition DSP and the alternation of speech recognition candidates makes operation easy. In this experiment, 8 speakers pronounced words of 1,300 vocabulary related to automatic exchange system over wire telephone network and the recognition system achieved 91.5% of word accuracy.