• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.905-913
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• 1987

This paper presents a speaker dependent isolated digit recognition algorithm using the Mellin transform. Since the Mellin transform converts a scale information into a phase information, attempts have been made to utilize this scale invariance property of the Mellin transform in order to alleviate a time-normalization procedure required for a speech recognition. It has been found that good results can be obtained by taking the Mellin transform to the features such as a ZCR, log energy, normalized autocorrelation coefficients, first predictor coefficient and normalized prediction error. We employed a difference function for evaluating a similarity between two patterns. When the proposed algorithm was tested on Korean digit words, a recognition rate of 83.3% was obtained. The recognition accuracy is not compatible with the other technique such as LPC distance however, it is believed that the Mellin transform can effectively perform the time-normalization processing for the speech recognition.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.567-575
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• 2001

This paper suggests new method to get fixed size parameter from different length of voice signals. The efficiency of speech recognizer is determined by how to compare the similarity(distance of each pattern) of the parameter from voice signal. But the variation of voice signal and the difference of speech speed make it difficult to extract the fixed size parameter from the voice signal. The method suggested in this paper is to normalize the parameter at fixed size by using the 2 dimension DCT(Discrete Cosine Transform) after representing the parameter by spectrogram. To prove validity of the suggested method, parameter extracted from 32 auditory filter-bank(it estimates auditory nerve firing probabilities) is used for the input of neural network after being processed by 2 dimension DCT. And to compare with conventional methods, we used one of conventional methods which solve time alignment problem. The result shows more efficient performance and faster recognition speed in the speaker dependent and independent isolated word recognition than conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.2
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• pp.196-205
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• 1991

This paper is a study on the isolated word recognition of speaker independent which proposes to newly improved MSVQ(multisection vector quantization) recognition system which improve the classical MSVQ recognition system. It is a difference that test pattern has on more section than reference pattern in recognition system 146 DDD area names are selected as recognition vocabulary. 12th LPC cepstral coefficients is used as feature parameter. and when codebook is generated, MINSUM and MINMAX are used in finding the centroid. According to the experiment result. it is proved that this method is better than VQ(vector quantization) recognition methods, DTW(dynamic time warping) pattern matching methods and classical MSVQ methods for recognition rate and recognition time.

• The Journal of the Acoustical Society of Korea
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• v.9 no.4
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• pp.66-76
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• 1990

In this work, the effect of noise and distortion of a telephone channel on the speech recognition is studied, and methods to improve the recognition rate are proposed. Computer simulation is done using the 100-word test data whichwere made by pronouncing ten times 100-phonetically balanced Korean isolated words in a speaker dependent mode. First, a spectral subtraction method is suggested to improve the noisy speech recognition. Then, the effect of bandwidth limiting and channel distortion is studied. It has been found that bandwidth limiting and amplitude distortion lower the recognition rate significantly, but phase distortion affects little. To reduce the channel effect, we modify the reference pattern according to some training data. When both channel noise and distortion exist, the recognition rate without the proposed method is merely 7.7~26.4%, but the recognition rate with the proposed method is drastically increased to 76.2~92.3%.

• The Journal of the Acoustical Society of Korea
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• v.19 no.6
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• pp.53-61
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• 2000

In the speech recognition under the noisy environments, reducing the mismatch introduced between training and testing environments is an important issue. Spectral subtraction is widely used technique because of its simplicity and relatively good performance in noisy environments. In this paper, we introduce histogram method as a reliable noise estimation approach for spectral subtraction. This method has advantages over the conventional noise estimation methods in that it does not need to detect non-speech intervals and it can estimate the noise spectra even in time-varying noise environments. Even though spectral subtraction is performed using a reliable average noise spectrum by the histogram method, considerable amount of residual noise remains due to the variations of instantaneous noise spectrum about mean. To overcome this limitation, we propose a new over-estimation technique based on distribution characteristics of histogram used for noise estimation. Since the proposed technique decides the degree of over-estimation adaptively according to the measured noise distribution, it has advantages to be few the influence of the SNR variation on the noise levels. According to speaker-independent isolated word recognition experiments in car noise environment under various SNR conditions, the proposed histogram-based over-estimation technique outperforms the conventional over-estimation technique.

• The Journal of the Acoustical Society of Korea
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• v.11 no.2
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• pp.73-84
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• 1992

This paper studies noise subtraction methods and distance measures for speech recognition in a noisy environment, and investigates noise robustness of the distance measures applied to the problem of isolated word recognition in white Gaussian and colored noise (vehicle noise) environments. Noise subtraction methods which can be used as a pre-processor for the speech recognition system, such as the spectral subtraction method, autocorrelation subtraction method, adaptive noise cancellation and acoustic beamforming are studied, and distance measures such and Log Likelihood Ratio ($d_{LLR}$), cepstral distance measure ($d_{CEP}$), weighted cepstral distance measure ($d_{WCEP}$), spectral slope distance measure ($d_{RPS}$) and cepstral projection distance measure ($d_{CP},\;d_{BCP},\;d_{WCP},\;d_{BWCP}$) are also investigated. Testing of the distance measures for speaker-dependent isolated word recognition in a noisy environment indicate that $d_{RPS}\;and\;d_{WCEP}$ which weigh higher order cepstral coefficients more heavily give considerable performance improvement over $d_{CEP}and\;d_{LLR}$. In addition, when no pre-emphasis is performed, the recognizer can maintain higher performance under high noise conditions.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.1
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• pp.28-33
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• 2009

In the research of speech recognition, locating the beginning and end of a speech utterance in a background of noise is of great importance. The conventional methods for speech endpoint detection are based on two simple time-domain measurements-short-time energy, and short-time zero-crossing rate, which couldn't guarantee the precise results if in the low signal-to-noise ratio environments. This paper proposes a novel approach that finds the Lyapunov exponent of time-domain waveform. This proposed method has no use for obtaining the frequency-domain parameters for endpoint detection process, e.g. Mel-Scale Features, which have been introduced in other paper. Accordingly, this algorithm is low complexity and suitable for Digital Isolated Word Recognition System.

• The Journal of the Acoustical Society of Korea
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• v.14 no.5
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• pp.5-10
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• 1995

In this paper we present a clustering procedure based on the use of HMM in order to get multiple statistical models which can well absorb the variants of each speaker with different ways of saying words. The HMM-clustered models obtained from the developed technique are applied to the speaker independent isolated word recognition. The HMM clustering method splits off all observation sequences with poor likelihood scores which fall below threshold from the training set and create a new model out of the observation sequences in the new cluster. Clustering is iterated by classifying each observation sequence as belonging to the cluster whose model has the maximum likelihood score. If any clutter has changed from the previous iteration the model in that cluster is reestimated by using the Baum-Welch reestimation procedure. Therefore, this method is more efficient than the conventional template-based clustering technique due to the integration capability of the clustering procedure and the parameter estimation. Experimental data show that the HMM-based clustering procedure leads to $1.43\%$ performance improvements over the conventional template-based clustering method and $2.08\%$ improvements over the single HMM method for the case of recognition of the isolated korean digits.

• The Journal of the Acoustical Society of Korea
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• v.13 no.2E
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• pp.58-67
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• 1994

Hidden Markov Model (HMM)-based algorithms have been used successfully in many speech recognition systems, especially large vocabulary systems. Although general purpose processors can be employed for the system, they inevitably suffer from the computational complexity and enormous data. Therefore, it is essential for real-time speech recognition to develop specialized hardware to accelerate the recognition steps. This paper concerns with a real-time implementation of an isolated word recognition system based on HMM. The speech recognition system consists of a host computer (PC), a DSP board, and a prototype Viterbi scoring board. The DSP board extracts feature vectors of speech signal. The Viterbi scoring board has been implemented using three field-programmable gate array chips. It employs a hardware-efficient Viterbi scoring architecture and performs the Viterbi algorithm for HMM-based speech recognition. At the clock rate of 10 MHz, the system can update about 100,000 states within a single frame of 10ms.

• The Journal of the Acoustical Society of Korea
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• v.24 no.8
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• pp.498-504
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• 2005

We introduce a Korean speech recognition platform (ECHOS) developed for education and research Purposes. ECHOS lowers the entry barrier to speech recognition research and can be used as a reference engine by providing elementary speech recognition modules. It has an easy simple object-oriented architecture, implemented in the C++ language with the standard template library. The input of the ECHOS is digital speech data sampled at 8 or 16 kHz. Its output is the 1-best recognition result. N-best recognition results, and a word graph. The recognition engine is composed of MFCC/PLP feature extraction, HMM-based acoustic modeling, n-gram language modeling, finite state network (FSN)- and lexical tree-based search algorithms. It can handle various tasks from isolated word recognition to large vocabulary continuous speech recognition. We compare the performance of ECHOS and hidden Markov model toolkit (HTK) for validation. In an FSN-based task. ECHOS shows similar word accuracy while the recognition time is doubled because of object-oriented implementation. For a 8000-word continuous speech recognition task, using the lexical tree search algorithm different from the algorithm used in HTK, it increases the word error rate by $40\%$ relatively but reduces the recognition time to half.