Multisection Vector Quantization Research Articles

The Effect of Fatigue on the Performance of Online Writer Recognition

The performance of biometric modalities based on things done by the subject, like signature and text-based recognition, may be affected by the subject’s state. Fatigue is one of the conditions that can significantly affect the outcome of handwriting tasks. Recent research has already shown that physical fatigue produces measurable differences in some features extracted from common writing and drawing tasks. It is important to establish to which extent physical fatigue contributes to the intra-person variability observed in these biometric modalities and also to know whether the performance of recognition methods is affected by fatigue. In this paper, we assess the impact of fatigue on intra-user variability and on the performance of signature-based and text-based writer recognition approaches encompassing both identification and verification. Several signature and text recognition methods are considered and applied to samples gathered after different levels of induced fatigue, measured by metabolic and mechanical assessment and also by subjective perception. The recognition methods are dynamic time warping and multi-section vector quantization, for signatures, and allographic text-dependent recognition for text in capital letters. For each fatigue level, the identification and verification performance of these methods is measured. Signature shows no statistically significant intra-user impact, but text does. On the other hand, performance of signature-based recognition approaches is negatively impacted by fatigue, whereas the impact is not noticeable in text-based recognition, provided long enough sequences are considered.

Real Time Implementation of Speaker Verification System

Verification of speakers is the use of the voice pattern to check the authenticity of the individual. In this paper the speaker verification system has been implemented on a real time DSP processor TMS3206713 with a new technique is proposed. Feature extraction and speaker modeling are the two major steps involved in speaker verification systems. Being the most frequently used features extracted from the human speech signal, the Mel Frequency Cepstral Coefficients (MFCC) have been considered for feature extraction. In the speaker modeling, a new technique known as Multi Section Vector Quantization (MSVQ) is implemented in addition to normal Vector Quantization. These techniques MFCC and VQ are generally used to recognize the speaker from a database of trained users. Using Multi Section Vector Quantization we can compare the trained codebook of particular user with the utterance and the authenticity decision can be made. Thus the advantage of using Multi Section Vector Quantization in speaker verification system is being discussed and the efficiency of this system is presented. To make this real time system robust in most of the environments a noise filter is also added during the pre-processing stage.

Real-time arrhythmia classification for large databases.

In this paper we introduce a coarse-to-fine arrhythmia classification technique that can be used for efficient processing of large Electrocardiogram (ECG) records. This technique reduces time-complexity of arrhythmia classification by reducing size of the beats as well as by quantizing the number of beats using Multi-Section Vector Quantization (MSVQ) without compromising on the accuracy of the classification. The proposed solution is tested on MIT-BIH arrhythmia database. This work achieves a highest computational speed-up factor of 2.2:1 in comparison with standard arrhythmia classification technique with marginal loss (<;1%) in classification accuracy. The clinical application of this technique enhances physician's throughput by factor of 2x while processing large ECG records from Holter system.

Speech Recognition Using MSVQ/TDRNN

본 논문에서는 MSVQ(Multi-Section Vector Quantization)와 시간지연 회귀 신경회로망(TDRNN)을 이용한 하이브리드 구조의 음성인식 방법을 제안한다. MSVQ는 음성의 길이를 일정한 구간 수로 정규화한 코드북을 생성하고, 시간지연 회귀 신경회로망은 이 코드북을 이용하여 음성을 인식한다. 시간지연 회귀 신경회로망은 음성의 시계열 문맥정보를 잘 학습할 수 있는 구조로 구성되었다. 음성특징으로 인지선형예측(PLP) 계수가 사용되었다. 음성인식 실험을 수행한 결과 MSVQ/TDRNN 음성인식기는 97.9 %의 화자독립 음성 인식률을 보였다. This paper presents a method for speech recognition using multi-section vector-quantization (MSVQ) and time-delay recurrent neural network (TDTNN). The MSVQ generates the codebook with normalized uniform sections of voice signal, and the TDRNN performs the speech recognition using the MSVQ codebook. The TDRNN is a time-delay recurrent neural network classifier with two different representations of dynamic context: the time-delayed input nodes represent local dynamic context, while the recursive nodes are able to represent long-term dynamic context of voice signal. The cepstral PLP coefficients were used as speech features. In the speech recognition experiments, the MSVQ/TDRNN speech recognizer shows 97.9 % word recognition rate for speaker independent recognition.

Fast on-line signature recognition based on VQ with time modeling

This paper proposes a multi-section vector quantization approach for on-line signature recognition. We have used the MCYT database, which consists of 330 users and 25 skilled forgeries per person performed by 5 different impostors. This database is larger than those typically used in the literature. Nevertheless, we also provide results from the SVC database. Our proposed system outperforms the winner of SVC with a reduced computational requirement, which is around 47 times lower than DTW. In addition, our system improves the database storage requirements due to vector compression, and is more privacy-friendly as it is not possible to recover the original signature using the codebooks. Experimental results with MCYT provide a 99.76% identification rate and 2.46% EER (skilled forgeries and individual threshold). Experimental results with SVC are 100% of identification rate and 0% (individual threshold) and 0.31% (general threshold) when using a two-section VQ approach.

Speech command recognition using a system based on the digital signal processor TMS320F2812 of Texas Instruments.

This paper describes the hardware design of a multi‐purpose electronic system for speech command recognition based on the digital signal processor TMS320F2812 of Texas Instruments. In the paper, we utilize this system for speech command recognition. Specifically, we design and build an electronic system capable of recognizing speech commands. The main blocks are a power source, oscillator, adc, amplifier, jtag, reset boot mode, and monitor LED. The technique used for recognition is a multi section vector quantization, which is a well known technique in the field. This was selected over techniques such as discrete hidden Markov models (HMMs), Gaussian mixture models, or dynamic time warping. It is faster and equally precise compared to these techniques for the purpose of a very limited vocabulary, although we are aware that several types of HMMs are the techniques more widely used until now for continuous speech recognition systems. Preliminary tests for five words give us a rate of 96% in recognition accuracy for 10 repetitions for training and 10 for testing (one subject, under a 20 dB of signal‐to‐noise ratio). In these months, the system will be tested for more words and subjects. Also, it will be tested in real situations.

Efficient on-line signature recognition based on multi-section vector quantization

This paper proposes a multi-section vector quantization approach for on-line signature recognition. We have used a database of 330 users which includes 25 skilled forgeries performed by 5 different impostors. This database is larger than those typically used in the literature. Nevertheless, we also provide results from the SVC database. Our proposed system obtains similar results as the state-of-the-art online signature recognition algorithm, Dynamic Time Warping, with a reduced computational requirement, around 47 times lower. In addition, our system improves the database storage requirements due to vector compression, and is more privacy-friendly because it is not possible to recover the original signature using the codebooks. Experimental results reveal that our proposed multi-section vector quantization achieves a 98% identification rate, minimum Detection Cost Function value equal to 2.29% for random forgeries and 7.75% for skilled forgeries.

DMS 모델과 퍼지 개념을 이용한 HMM에 기초를 둔 음성 인식

본 논문은 화자 독립의 음성인식을 위한 연구로서, DMS(Dynamic Multi-Section) 모델에 의한 DMSVQ(Dynamic Multi-Section Vector Quantization) 코드북과 퍼지 개념을 이용한 HMM(Hidden Markov Model) 음성인식 방법을 제안한다. 제안된 인식 방법에서는 학습 데이터를 동적으로 몇 개의 구간(section)으로 분할한 후, 각 구간마다 DMSVQ 코드북(codebook)으로 부터 거리값이 작은 순으로 퍼지 법칙을 적용함으로써 적당한 확률값을 준 다중 관측열(multi-observation sequences)을 구한다. 그런 다음, 이 다중 관측열을 이용하여 HMM을 작성하고, 인식시에는 관측 확률값이 가장 높은 것을 인식된 것으로 선택한다. 제안된 방법에 의한 인식 실험은 기존의 다양한 인식 실험들과 비교를 위해 동일한 조건하에서 같은 데이터로 수행 하였다. 실험 결과로서, 본 연구에서 제안한 방법이 기존의 방법들보다 우수한 방법임을 입증하였다. This paper proposes a HMM-based recognition method using DMSVQ(Dynamic Multi-Section Vector Quantization) codebook by DMS(Dynamic Multi-Section) model and fuzzy concept, as a study for speaker- independent speech recognition. In this proposed recognition method, training data are divided into several dynamic section and multi-observation sequences which are given proper probabilities by fuzzy rule according to order of short distance from DMSVQ codebook per each section are obtained. Thereafter, the HMM using this multi-observation sequences is generated, and in case of recognition, a word that has the most highest probability is selected as a recognized word. Other experiments to compare with the results of recognition experiments using proposed method are implemented as a data by the various conventional recognition methods under the equivalent environment. Through the experiment results, it is proved that the proposed method in this study is superior to the conventional recognition methods.

Speech coding for fast speech commands recognition

Isolated speech recognition could be more appropriate than continuous speech recognition systems for some word command applications. In this paper, some speech coders are explored that can be useful for fast word command recognition using low memory storage. First, decimated critical bands vectors by frame are used as coding features. The accuracy is around 94% for a set of ten commands; they are used at first step. After that, using a reduced DTW classification scheme, the accuracy for a small subset of possible candidates is above 99%. Another coding scheme is using the Karhunen–Loeve Transform (KLT) combined with multisection vector quantization. The KLT is not a fast transform; however, used at the subword level it gives a fast recognizer with accuracy around 99%. The classifiers in both cases are fast, around a second, and require low memory storage.

EFFICIENT SPEECH RECOGNITION TECHNIQUES FOR THE FINALS OF MANDARIN SYLLABLES

A long-term research project toward Mandarin speech recognition techniques for very large vocabulary and unlimited text is considered. By carefully examining the special structures of Chinese language, the first-stage goal is set to be the design of efficient techniques to recognize the finals of Mandarin syllables. In this paper, three special approaches to do this are proposed. The Segmental Model Approach defines the final models by dividing the finals into several segments according to the acoustic structures of the speech signals. The Three-pass Approach uses three consecutive passes to classify the finals into small sets and improve the recognition efficiency. The Multi-section Vector Quantization (MSVQ) Approach, on the other hand, significantly reduces the necessary computation time by incorporating the branch-and-bound algorithm and common codebook concept with the MSVQ techniques. Extensive computer simulations are performed first to optimize each approach by choosing the best set of parameters then to compare the performance of the three approaches. It was found that all the three approaches are very efficient in terms of relatively high recognition rate and short computation time, and the MSVQ Approach provides the highest recognition rate at the shortest computation time, thus it is most attractive.

Isolated-word speech recognition using multisection vector quantization codebooks

A new approach to isolated-word speech recognition using vector quantization (VQ) is examined. In this approach, words are recognized by means of sequences of VQ codebooks, called multisection codebooks. A separate multisection codebook is designed for each word in the recognition vocabulary by dividing the word into equal-length sections and designing a standard VQ codebook for each section. Unknown words are classified by dividing them into corresponding sections, encoding them with the multisection codebooks, and finding the multisection codebook that yields the smallest average distortion. For speaker-independent recognition of the digits, this approach achieved a recognition accuracy of 98 percent. In addition, the approach achieved greater than 99 percent accuracy for speaker-dependent recognition of the digits with only one distortion computation per input frame per vocabulary word. The approach is described, detailed experimental results are presented and discussed, and computational requirements are analyzed.

Speaker-dependent isolated word recognition using speaker-independent vector quantization codebooks augmented with speaker-specific data

A hybrid approach to speaker-dependent isolated word recognition is discussed. The approach merges speaker-specific information obtained from a single training utterance with multisection vector quantization codebooks that were designed for speaker-independent recognition. The approach provides easily trained, computationally efficient, and accurate isolated word recognition. On the digits, the approach achieved an error rate less than 1 percent.