In this paper, we present a robust on-line algorithm for real-time audio-to-score alignment based on a delayed decision and anticipation framework. We employ Segmental Conditional Random Fields and Linear Dynamical System to model musical performance. The combination of these models allows an efficient iterative decoding of score position and tempo. The combined advantages of our approach are the delayed-decision Viterbi algorithm which utilizes future information to determine past score position with high reliability, thus improving alignment accuracy, and the fact that the future position can be anticipated using an adaptively estimated tempo. Experiments using classical music and jazz databases demonstrate the validity of our approach.

In this paper, we propose separable lattice hidden Markov models, in which multiple hidden state sequences interact to model the observation on a lattice. The proposed model can be efficiently applied for modeling images, image sequences, 3-D object models and higher dimensional applications, due to the composite structure of Markov chains which reduces the complexity while retaining good properties for multi-dimensional data. In case of 2-D lattices, the proposed model performs an elastic matching in both horizontal and vertical directions; this makes it possible to model not only invariances to the size and location of an object but also nonlinear warping in each dimension. We present a training algorithm for separable lattice HMMs based on a variational approximation. Moreover, the deterministic annealing EM (DAEM) algorithm was applied to the variational algorithm for separable lattice HMMs. Face recognition experiments on the XM2VTS database show that the proposed model has good properties for face image modeling.

In the present paper, the Monte Carlo EM (MCEM) algorithm with a Gibbs sampler is applied for estimating parameters of a trajectory HMM, which has been derived from an HMM by imposing explicit relationships between static and dynamic features. The trajectory HMM can alleviate two limitations of the HMM, which are i) constant statistics within a state, and ii) conditional independence of state output probabilities, without increasing the number of model parameters. In a speaker-dependent continuous speech recognition experiment, trajectory HMMs estimated by the MCEM algorithm achieved significant improvements over the corresponding HMMs trained by the EM (Baum-Welch) algorithm.

Abstract Recently, a trajectory model, derived from the hiddenMarkov model (HMM) by imposing explicit relationshipsbetween static and dynamic features, has been proposed.The derived model, named trajectory HMM , can alleviatetwo limitations of the HMM: constant statistics within astate and conditional independence assumption of state out-put probabilities. In the present paper, a speaker adapta-tion algorithm for the trajectory HMM based on feature-space Maximum Likelihood Linear Regression (fMLLR)is derived and evaluated. Results of a simple continu-ous speech recognition experiment shows that adapting tra-jectory HMMs using the derived adaptation algorithm im-proves the speech recognition performance. Index Terms : trajectory HMM, adaptation, fMLLR. 1. Introduction Speech recognition technologies have achieved significantprogress with the introduction of hidden Markov models(HMMs). Their tractability and efficient implementationsare achieved by a number of assumptions, such as constantstatistics within an HMM state, conditional independenceof state output probabilities. Although these assumptionsmake the HMM practically useful, they are not realistic formodeling sequences of speech spectra, especially in spon-taneous speech. To overcome these shortcomings of theHMM, a variety of alternative models have been proposed,e.g., [1–3]. Although these models can improve the speechrecognition performance, they generally require an increaseinthenumberofmodelparametersandcomputationalcom-plexity. Alternatively, the use of dynamic features (deltaand delta-delta features) [4] also improves the performanceof HMM-based speech recognizers. It can be viewed as asimple mechanism to capture time dependencies. However,it has been thought of as an ad hoc rather than an essentialsolution. Generally, dynamic features are calculated as re-gression coefficients from their neighboring static features.Therefore, relationshipsbetweenstaticanddynamicfeaturevector sequences are deterministic. However, usually theserelationships are ignored and the static and dynamic fea-tures are modeled as independent random variables. Ignor-ing these dependencies allows inconsistency between thestatic and dynamic features when the HMM is used as agenerative model in the obvious way.Recently, a trajectory model, derived from the HMM byimposing the explicit relationships between static and dy-namic features, has been proposed [5]. The derived model,named

This paper describes an online discriminative training algorithm aiming at achieving speaker identification on interactive robots. A robot incrementally acquires speakers' voice characteristics during the interaction with the speakers. We simulate the situation that the speakers never give their IDs and the robot can only know whether the identification decision was correct or not from the speaker's positive or negative behavioral reaction. The speaker models are adjusted based on this limited information using minimum classification error (MCE) training consisting of positive and negative adaptation. In cases of correct identification, the conventional MCE training algorithm can be used. We compare three kinds of negative adaptation algorithms for the cases of incorrect identification. Experimental results show that the combination of the positive and negative adaptation achieves faster convergence, and negative adaptation which adjusts only a misclassified speaker model reaches an identification rate of 80% four times faster than the positive adaptation alone.

This paper presents an analysis of the applicability of sparse kernel principal component analysis (SKPCA) for feature extraction in speech recognition, as well as a proposed approach to make the SKPCA technique realizable for a large amount of training data, which is a usual context in speech recognition systems. Although the KPCA (kernel principal component analysis) has proved to be an efficient technique for being applied to speech recognition, it has the disadvantage of requiring training data reduction, when its amount is excessively large. The standard approach to perform this data reduction is to randomly choose frames from the original data set, which does not necessarily provide a good statistical representation of the original data set. In order to solve this problem a likelihood related re-estimation procedure was applied to the KPCA framework, thus creating the SKPCA. The experimental results show the efficiency of SKPCA technique with the proposed approach over the KPCA with the standard sparse solution using randomly chosen frames and the standard feature extraction techniques.

This paper investigates the parameter tying strategies of mixtures of factor analyzers (MFA) and discriminative training of MFA for speaker identification. The parameters of factor loading matrices or diagonal matrices are shared in different mixtures of MFA. The minimum classification error (MCE) training is applied to the MFA parameters to enhance the discrimination abilities. The results of text-independent speaker identification experiments show that MFA outperforms the conventional Gaussian mixture models (GMM) with diagonal or full covariance matrices and achieves the best performance when sharing the diagonal matrices, resulting in a relative gain of 26% over the GMM with diagonal covariance matrices. The recognition performance is further improved by the MCE training with an additional 3% error reduction.

This paper introduces a Viterbi algorithm to obtain a sub-optimal state sequence for trajectory-HMM, which is derived from HMM with explicit relationship between static and dynamic features. The trajectory-HMM can alleviate some limitations of HMM, which are (i) constant statistics within HMM state and (ii) conditional independence of observations given the state sequence, without increasing the number of model parameters. The proposed algorithm was applied to state-boundary optimization for Viterbi training and N-best rescoring. In a speaker-dependent continuous speech recognition experiment, trajectory-HMM with the proposed algorithm achieved about 14% error reduction over the standard HMM with the conventional Viterbi algorithm.

ABSTRACT This paper investigates the effectiveness of the DAEM (Determin-istic Annealing EM) algorithm in acoustic modeling for speakerand speech recognition. Although the EM algorithm has beenwidely used to approximate the ML estimates, it has the problemof initialization dependence. To relax this problem, the DAEMalgorithm has been proposed and confirmed the effectiveness insmall tasks. In this paper, we applied the DAEM algorithm tospeakerrecognitionbasedonGMMsandcontinuousspeechrecog-nitionbasedonHMMs. ExperimentalresultsshowthattheDAEMalgorithm can improve the recognition performance as comparedtotheordinaryEMalgorithmwithconventionalinitializationmeth-ods,especiallyintheflatstarttrainingforcontinuousspeechrecog-nition. 1. INTRODUCTION The EM (Expectation-Maximization) algorithm [1] is widelyused for parameter estimation of statistical models with hiddenvariables. This algorithm provides a simple iterative proceduretoobtainapproximateML(maximumlikelihood)estimates. How-ever, since the EM algorithm is a hill-climbing approach, it suffersfrom the local maxima problem.On the other hand, GMMs (Gaussian mixture models) [2] andHMMs (hidden Markov models) [3] have been commonly usedin acoustic modeling for speaker and speech recognition, respec-tively. In conventional approaches, the LBG algorithm for GMMsand the segmental k-means algorithm for HMMs have been em-ployed to obtain initial model parameters before applying the EMalgorithm. However these initial values are not guaranteed to benear the true maximum likelihood point, and the posterior den-sity becomes unreliable at an early stage of training. Especiallyin continuous speech recognition, it is difficult to obtain accuratephoneme boundaries for all training data. Hence, the embeddedtraininghasbeenusedinwhichphonemeboundariesarealsodealtas hidden variables, and estimated based on the EM algorithm.Furthermore, in the worse case that the boundary information isnot available, a method called the flat start training is often ap-plied. In this method, initial parameters of HMMs are given bymaking all states of all models equal, and then carry out the em-bedded training. In these situations, we do not have enough priorknowledge to obtain a good initial values for the EM algorithm,and it would converge to one of the local maxima or saddle points

This paper proposes a decision-tree backing-off technique for an HMM-based speech synthesis system. In the system, a decision-tree based context clustering technique is used for constructing parameter tying structures. In the context clustering, the MDL criterion has been used as a stopping criterion. In this paper, however, huge decision-trees are constructed without any stopping criterion. In the synthesis phase, decision-trees obtained in this way are used in the proposed backing-off scheme. This enables us to adjust the cluster size dynamically at runtime according to the text to be synthesized. Results of subjective listening tests show that the proposed technique improves the synthesized speech quality.

This paper describes an emotional speech synthesis system based on HMMs and related modeling techniques. For concatenative speech synthesis, we require all of the concatenation units that will be used to be recorded beforehand and made available at synthesis time. To adopt this approach for synthesizing the wide variety of human emotions possible in speech, implies that this process should be repeated for every targeted emotion making this task challenging and time consuming. In this paper, we propose an emotional speech synthesis technique based on HMMs, especially for the case where only limited amount of training data is available, directly incorporating subjective evaluation results performed on the training data. Listening results performed on the synthesized speech suggest that the proposed technique helps to improve the emotional content of synthesized speech.

In the present paper, a hidden-semi Markov model (HSMM) based speech synthesis system is proposed. In a hidden Markov model (HMM) based speech synthesis system which we have proposed, rhythm and tempo are controlled by state duration probability distributions modeled by single Gaussian distributions. To synthesis speech, it constructs a sentence HMM corresponding to an arbitralily given text and determine state durations maximizing their probabilities, then a speech parameter vector sequence is generated for the given state sequence. However, there is an inconsistency: although the speech is synthesized from HMMs with explicit state duration probability distributions, HMMs are trained without them. In the present paper, we introduce an HSMM, which is an HMM with explicit state duration probability distributions, into the HMM-based speech synthesis system. Experimental results show that the use of HSMM training improves the naturalness of the synthesized speech.

In this paper, we define an F0 quantization scheme for a very low bit rate speech coder based on HMM (hidden Markov model). In the coding system, the encoder carries out phoneme recognition, and transmits phoneme indices, state durations and F0 information to the decoder. In the decoder, phoneme HMM are concatenated according to the phoneme indices, and a sequence of mel-cepstral coefficient vectors is generated from the concatenated HMM. Finally we obtain synthetic speech by using the MLSA (mel log spectrum approximation) filter according to the mel-cepstral coefficients and F0 information. In addition to the F0 quantization, we investigate encoding methods for other parameters to reduce the bit rate, yet keeping the subjective speech quality. A subjective listening test shows that the performance of the proposed coder at about 100/spl sim/150 bit/s is superior to a VQ-based vocoder at 600 bit/s (mel-cepstrum: 6 bit/frame/spl times/50 frame/s, F0: 6 bit/frame/spl times/50 frame/s).

This paper proposes a speech recognition technique based on acoustic models considering voice characteristic variations. Context-dependent acoustic models, which are typically triphone HMM, are often used in continuous speech recognition systems. This work hypothesizes that the speaker voice characteristics that humans can perceive by listening are also factors in acoustic variation for construction of acoustic models, and a tree-based clustering technique is also applied to speaker voice characteristics to construct voice-characteristic-dependent acoustic models. In speech recognition using triphone models, the neighboring phonetic context is given from the linguistic-phonetic knowledge in advance; in contrast, the voice characteristics of input speech are unknown in recognition using voice-characteristic-dependent acoustic models. This paper proposes a method of recognizing speech even under conditions where the voice characteristics of the input speech are unknown. The result of a gender-dependent speech recognition experiment shows that the proposed method achieves higher recognition performance in comparison to conventional methods.

This paper describes an approachfor feature extraction in speech recognition systems using kernel principal componentanalysis (KPCA). This approachconsists in representing speech features as the projection of the extracted speech features mapped into a feature space via a nonlinear mapping onto the principal components. The nonlinear mapping is implicitly performed using the kerneltrick, which is an useful way of not mapping the input space into a featurespace explicitly,makingthis mapping computationally feasible. Better results were obtained by using this approach when compared to the standard technique.

This paper describes the development of a Brazilian Portuguese text-to-speech system which applies a technique wherein speech is directly synthesized from hidden Markov models. In order to build the synthesizer a speech database was recorded and phonetically segmented. Furthermore, contextual informations about syllables, words, phrases, and utterances were determined, as well as questions for decision tree-based context clustering algorithms. The resulting system presents a fair reproduction of the prosody even when a small database is used for training.

This paper shows that the HMM whose state output vector includes static and dynamic feature parameters can be reformulated as a trajectory model by imposing the explicit relationship between the static and dynamic features. The derived model, named trajectory HMM, can alleviate the limitations of HMMs: i) constant statistics within an HMM state and ii) independence assumption of state output probabilities. We also derive a Viterbi-type training algorithm for the trajectory HMM. A preliminary speech recognition experiment based on N-best rescoring demonstrates that the training algorithm can improve the recognition performance significantly even though the trajectory HMM has the same parameterization as the standard HMM.

In this paper, a new decision tree-based clustering technique called Phonetic, Dimensional and State Positional Decision Tree (PDS-DT) is proposed. In PDS-DT, phonetic contexts, dimensions and state positions are grouped simultaneously during decision tree construction. PDS-DT provides a complicate distribution sharing structure without any external control parameters. In speaker-independent continuous speech recognition experiments, PDS-DT achieved about 13%--15% error reduction over the phonetic decision tree-based state-tying technique.

The purpose of this study is to describe a method of how physiological simulation and diagnosis for the cardiovascular system can be achieved in clinical use. The circulatory system model aims to represent various changes or diseases based on experimental measurements. In respect with some designed levels, the circulatory system could be modeled into a target granularity of sizes, resolutions, or timescales. The macro model contains a wide range of physiological information for various subsystems within the whole body, but it is impossible to be detailed into small time units for time-dependent variables. Whereas, the micro model enables to analyze microscopic behavior only for a single subsystem, because the extrafunctions of the whole body are omitted. Therefore, the proposed physiological simulation method integrates macro circulatory and micro cardiovascular system models, so that long-term macroscopic body conditions and short-term microscopic blood flow could be concurrently simulated.

A six-layered architecture for behavior control, consciousness-based architecture (CBA), is developed to link symbolic behaviors to low-level behaviors such as reflex action, detour and pursuit. This architecture makes possible behaviors categorized in "use of media" in parallel with running low-level behaviors. Two emotion-valued criteria are given for vertical and horizontal behavior selection. While a level of behavior is chosen to maximize the consciousness intensity, a behavior at the level chosen is selected to increase the pleasure. In CBA, the obstruction of behavior triggers the elevation of the level of consciousness. Computer simulation successfully shows symbolic detour of an obstacle and enclosure of a prey by three creatures predicting the positions of mates, a prey and an obstacle. CBA also shows its advantage over a Q-learning technique for the obstacle detouring problem.

Split Phonetic Decision Tree (DS-PDT) is proposed. In DSPDT, state distributions are split dimensionally when applying phonetic question. This technique is an extension of the decision tree based acoustic modeling. It gives a proper context-dependent sharing structure of each dimension automatically while maintaining the correlations among the dimensions. In speaker-independent continuous speech recognition experiments, DS-PDT achieved about 8% error reduction over the phonetic decision tree clustering.

This article explains a modeling methodology of human physiology. An agent-based approach to a physiological digital human is proposed, and its design concept and architectural framework are discussed. The modeling is based on two types of agents: stationary agents which model human physiology, and mobile agents that model parameter interaction between the user and the human physiological model. The human thermoregulatory system is chosen as an example of model functions. A Java-based agent system is shown as an implementation of the model.

Presents an approach to modeling speech spectra and pitch for text-independent speaker identification using Gaussian mixture models based on multi-space probability distribution (MSD-GMM). The MSD-GMM allows us to model continuous pitch values for voiced frames and discrete symbols representing unvoiced frames in a unified framework. Spectral and pitch features are jointly modeled by a two-stream MSD-GMM. We derive maximum likelihood estimation formulae for the MSD-GMM parameters, and the MSD-GMM speaker models are evaluated for text-independent speaker identification tasks. Experimental results, show that the MSD-GMM can efficiently model spectral and pitch features of each speaker and outperforms conventional speaker models.

In our previous work, we have proposed a speaker modeling technique using spectral and pitch features for text-independent speaker identification based on Multi-Space Probability Distribution Gaussian Mixture Models (MSD-GMMs). We have presented a maximum likelihood (ML) estimation procedure for the MSD-GMM parameters and demonstrated its high recognition performance. In this paper, we describe an minimum classification error (MCE) training procedure for the MSDGMM speaker models. MCE training is also applied to automatically estimate mixture-dependent stream weights for spectral and pitch streams. The MCE-based MSD-GMM speaker models are evaluated for a text-independent speaker identification task. Experimental results show that MCE training of the MSD-GMM parameters significantly reduces identification errors and system performance is further improved by appropriately weighting spectral and pitch streams using MCE training.

This paper describes improvements on the excitation model of an HMM-based text-to-speech system. In our previous work, natural sounding speech can be synthesized from trained HMMs. However, it has a typical quality of “vocoded speech” since the system uses a traditional excitation model with either a periodic impulse train or white noise. In this paper, in order to reduce the synthetic quality, a mixed excitation model used in MELP is incorporated into the system. Excitation parameters used in mixed excitation are modeled by HMMs, and generated from HMMs by a parameter generation algorithm in the synthesis phase. The result of a listening test shows that the mixed excitation model significantly improves quality of synthesized speech as compared with the traditional excitation model.

This paper presents a diagnostic Support system for a large-scale circulatory model, which is a dynamic model called Human, consisting of 25 functional modules with equations of about 1000 lines, over 200 variables, and over 100 static parameters. The Human model simulates circulatory behavior on standard physiology textbook. In this paper, we describe a diagnostic support method of understanding model structure about equations, variables, and parameters. The proposed method 1) generates cause and effect relationships between variables, 2) visualizes hierarchical directed graph of variables, and 3) enables to track paths in the hierarchy of the circulatory model.

We designed consciousness-based architecture (CBA), a six-layered hierarchy of consciousness to link emotion-based behaviors including reflex action, approach, detour to symbolic behaviors, such as ambush with use of media in an environment. The design principle of CBA is that it should have a function/structure that easily makes it possible to reflect the user's own subjective experiences in behavior design. On this principle, CBA enables a designer to map his/her subjective reflection about his/her behavior selection process to CBA in terms of emotion. In CBA, therefore, while the use of objective knowledge about human/animal behaviors is limited to the evolutionary framework of CBA, we follow Husserlian phenomenology to describe internal states of the architecture using components of human consciousness, such as emotion and perception, rather than physical, endogenous parameters. In CBA, a level of behavior is chosen at which an emotion-based degree of the consciousness is maximal, and a behavior at the chosen level is selected to maximize the criterion of pleasure. Model design was tested using two Khepera robots, and ambush with a limited use of temporal and spatial information of an environment was shown.

This paper derives a speech parameter generation algorithm for HMM-based speech synthesis, in which the speech parameter sequence is generated from HMMs whose observation vector consists of a spectral parameter vector and its dynamic feature vectors. In the algorithm, we assume that the state sequence (state and mixture sequence for the multi-mixture case) or a part of the state sequence is unobservable (i.e., hidden or latent). As a result, the algorithm iterates the forward-backward algorithm and the parameter generation algorithm for the case where the state sequence is given. Experimental results show that by using the algorithm, we can reproduce clear formant structure from multi-mixture HMMs as compared with that produced from single-mixture HMMs.

This paper presents an approach to estimating the parameters of continuous density HMMs for visual speech recognition. One of the key issues of image-based visual speech recognition is normalization of lip location and lighting conditions prior to estimating the parameters of HMMs. We presented a normalized training method in which the normalization process is integrated in the model training. This paper extends it for contrast normalization in addition to average-intensity and location normalization. The proposed method provides a theoretically-well-defined algorithm based on a maximum likelihood formulation, hence the likelihood for the training data is guaranteed to increase at each iteration of the normalized training. Experiments on the M2VTS database show that the recognition performance can be significantly improved by the normalized training.

This paper describes an HMM-based lip location normalization process, in order to improve the recognition performance in automatic lip-reading. This paper uses the image-based method in order to represent the lip visual information. One of the most critical factors which affect the recognition results in image-based method is the position of lips in frames. This paper describes a method to normalize the lip location which is similar to SAT (speaker adaptive training), and presents several experiments which were carried out in order to measure the effectiveness of the proposed method. Experiments of isolated words with and without the original movement from speakers were carried out on the M2VTS database.

Today’s automated telephone services generally use recorded speech from one speaker for all output. In applications with large and varying output vocabularies, such as place names, it may be necessary to employ a second speaker to provide new vocabulary items if the original speaker is not available, or to use text-tospeech (TTS) synthesis for the whole or parts of the output. This paper reports a comparison of 10 schemes for the generation of spoken output in a travel information service, ranging from natural speech from a single speaker, through combinations of different voices and of natural and synthetic speech, to TTS synthesis throughout. The results show strong preferences for concatenated speech over TTS and for professionalquality recordings over amateur ones, and a weaker preference for a single speaker over two speakers.

This paper presents a very low bit rate speech coder based on HMM (hidden Markov model). The encoder carries out phoneme recognition, and transmits phoneme indexes, state durations and pitch information to the decoder. In the decoder, phoneme HMMs are concatenated according to the phoneme indexes, and a sequence of mel-cepstral coefficient vectors is generated from the concatenated HMM by using an ML-based speech parameter generation technique. Finally we obtain synthetic speech by exciting the MLSA (mel log spectrum approximation) filter, whose coefficients are given by mel-cepstral coefficients, according to the pitch information. A subjective listening test shows that the performance of the proposed coder at about 150 bit/s (for the test data including 26% silence region) is comparable to a VQ-based vocoder at 400 bit/s (=8 bit/frame/spl times/50 frame/s) without pitch quantization for both coders.

This paper describes intensity and location normalization techniques for improving the performance of visual speech recognizers used in audio-visual speech recognition. For auditory speech recognition, there exist many methods for dealing with channel characteristics and speaker individualities, e.g., CMN (cepstral mean normalization), SAT (speaker adaptive training). We present two techniques similar to CMN and SAT, respectively, for intensity and location normalization in visual speech recognition. Word recognition experiments based on HMM show that a significant improvement in recogniton performance is achieved by combining the two techniques.

This paper describes a speaker recognition model using Two-Dimensional Mel-Cepstrum and predictive neural network.The speaker model consists of two networks.The rst one is a self-organizing VQ map(Kohonen's feature map).The second part is the predictive network and learns transitional patterns on the feature map of each speaker's model.TDMC consists of averaged features and dynamic features of the two-dimensional mel-log spectra in the analyzed interval.The measure for speaker recognition is obtained by using a combination of the VQ distortion on the feature map and the prediction error on the predictive network.In the study, text-independent speaker identication experiments for 8 speakers were carried out.The experimental results have shown that a combination of a feature map and a predictive network is very eective, and that the proposed model using TDMC shows the robustness for time interval.

this paper, the influences of such non-linearity and network topology on network performances are studied (see Figures 1 & 2). The three common classes of speech degradation, namely non-linear degradation introduced by CELP, additive noise including GWN (Gaussian white noise) and two real-world noises, and linear system degradation are considered. In addition, we also investigate whether or not the enhancer characteristics depend on the class of degradation.

The CAPTAIN system is a VIDEOTEX system in Japan. Existing telephone lines are used for transmission and home TV sets are used for the display units. The first experimental service (with 1000 user terminals, 199 information providers, and 100 000 displays frames) was carried out from December 1979 to March 1981 by the Ministry of Posts and Telecommunications and the Nippon Telegraph and Telephone Public Corporation. Based on surveys carried out to obtain opinions on the system, the first experimental system was improved and the second experimental service (with 2000 user terminals, 200 000 display frames) was started in August 1981. The system uses the pattern transmission method so as to display pictures precisely. This method transmits dot pattern information intact between a center and home terminals, allowing the system to display not only handwritten pictures but also complex shaped characters, such as Kanji (Chinese ideograph) characters, Arabic characters, Korean syllabary, etc. According to a questionnaire sent to users, many want to utilize this system when the commercial service starts. Display attractiveness is evaluated highly by these users, verifying the effectiveness of the pattern transmission method. This paper describes: experimental service outline system functions and new techniques for this system (especially display characteristics and transmission method) user reaction to this system