Currently, population in Japan has been aging at a speed unparalleled in other countries, and countermeasures against aging population and the worsening of disease for people with disabilities have become urgent issues. Pneumonia and aspiration pneumonia are the leading causes of death. It is said that swallowing function tends to decline from around age of 40, however it is important to keep it in good condition without deteriorating function as much as possible. The gold standard for swallowing functional evaluation is swallowing contrast testing, however X-ray exposure disables to repeat testing. In addition, Repetitive Saliva Swallowing Test (RSST) of screening test is difficult for self-check. Therefore, in this study, we develop a system to self-evaluate swallowing ability for keeping swallowing function healthy. It is proposed by applying optical flow and artificial intelligence of DeepLabcut. As a result, we were able to visualize movement of larynx during swallowing.

This paper regards social question-and-answer (Q&A) collections such as Yahoo! Answers as knowledge repositories and investigates techniques to mine knowledge from them to improve sentence-based complex question answering (QA) systems. Specifically, we present a question-type-specific method (QTSM) that extracts question-type-dependent cue expressions from social Q&A pairs in which the question types are the same as the submitted questions. We compare our approach with the question-specific and monolingual translation-based methods presented in previous works. The question-specific method (QSM) extracts question-dependent answer words from social Q&A pairs in which the questions resemble the submitted question. The monolingual translation-based method (MTM) learns word-to-word translation probabilities from all of the social Q&A pairs without considering the question or its type. Experiments on the extension of the NTCIR 2008 Chinese test data set demonstrate that our models that exploit social Q&A collections are significantly more effective than baseline methods such as LexRank. The performance ranking of these methods is QTSM > {QSM, MTM}. The largest F3 improvements in our proposed QTSM over QSM and MTM reach 6.0% and 5.8%, respectively.

An ensemble speaker and speaking environment modeling (ESSEM) approach was recently developed. This ESSEM process consists of offline and online phases. The offline phase establishes an environment structure using speech data collected under a wide range of acoustic conditions, whereas the online phase estimates a set of acoustic models that matches the testing environment based on the established environment structure. Since the estimated acoustic models accurately characterize particular testing conditions, ESSEM can improve the speech recognition performance under adverse conditions. In this work, we propose two maximum a posteriori (MAP) based algorithms to improve the online estimation part of the original ESSEM framework. We first develop MAP-based environment structure adaptation to refine the original environment structure. Next, we propose to utilize the MAP criterion to estimate the mapping function of ESSEM and enhance the environment modeling capability. For the MAP estimation, three types of priors are derived; they are the clustered prior (CP), the sequential prior (SP), and the hierarchical prior (HP) densities. Since each prior density is able to characterize specific acoustic knowledge, we further derive a combination mechanism to integrate the three priors. Based on the experimental results on the Aurora-2 task, we verify that using the MAP-based online mapping function estimation can enable ESSEM to achieve better performance than using the maximum-likelihood (ML) based counterpart. Moreover, by using an integration of the online environment structuring adaptation and mapping function estimation, the proposed MAP-based ESSEM framework is found to provide the best performance. Compared with our baseline results, MAP-based ESSEM achieves an average word error rate reduction of 15.53% (5.41 to 4.57%) under 50 testing conditions at a signal-to-noise ratio (SNR) of 0 to 20 dB over the three standardized testing sets.

This study presents a novel approach to spoken document retrieval based on neural probabilistic language modeling for semantic inference. The neural network based language model is applied to estimate word association in a continuous space. The different kinds of weighting schemes are investigated to represent recognized words of a spoken document into an indexing vector. The indexing vector is transferred into the semantic indexing vector through the neural probabilistic language model. Such a semantic word inference and re-weighting make the semantic indexing vector a suitable representation for speech indexing. Experimental results conducted on Mandarin Chinese broadcast news show that the proposed approach can achieve a substantial and consistent improvement of spoken document retrieval.

Speaker clustering has been widely adopted for clustering the speech data based on acoustic characteristics so that an unsupervised speaker normalization and speaker adaptive training can be applied for a better speech recognition performance. In this study, we present a vector space speaker clustering approach with long-term feature analysis. The supervector based on the GMM mean vectors is adopted to represent the characteristics of speakers. To achieve a robust representation, total variability subspace modeling, which has been successfully applied in speaker recognition for compensating channel and session variability over the GMM mean supervector, is used for speaker clustering. We apply a long-term feature analysis strategy to average short-time spectral features over a period of time to capture the speaker traits that are manifested over a speech segment longer than a spectral frame. Experiments conducted on lecture style speech show that this speaker clustering approach offers a better speech recognition performance.

This paper presents a joint analysis approach to address the acoustic feature normalization for robust speech recognition. The variations in acoustic environments and speakers are the major challenge for speech recognition. The conventional normalizations of these two variations are separately processed, applying the speaker normalization with an assumption of a noise free condition and applying the noise compensation with an assumption of speaker independency, and thus resulting in a suboptimal performance. The proposed joint analysis approach simultaneously considers the vocal tract length normalization and averaged temporal information of cepstral features. In a data-driven manner, the Gaussian mixture model is used to estimate the conditional parameters in the joint analysis. Experimental results show that the proposed approach achieves a substantial improvement.

Deep neural networks (DNNs) have been successfully applied to automatic speech recognition (ASR). However, no study has investigated the possibility of building a language-independent sub-network DNN as the basis for further training of any new language using a simple plug-in of the sub-network. In this paper, we propose a novel technique to split a DNN into language-independent and -dependent sub-networks using multi-lingual speech training data. Our basic assumption is that, in a DNN for speech processing, language-independent feature processing is done in stages that are near to the input layer, while language-dependent processing is performed in stages that are near to the output layer. Based on this assumption, we propose a technique to simultaneously optimize multiple sub-networks in a DNN trained with multi-lingual speech data. The language-dependent and -independent processing boundaries in individual sub-networks are segmented automatically. We test our technique in phoneme classification experiments. The results demonstrate that a language-independent sub-network DNN extracted by our technique can be used as a universal network for speech processing of additional new languages.

This study presents an overview of VoiceTra, which was developed by NICT and released as the world's first network-based multilingual speech-to-speech translation system for smartphones, and describes in detail its multilingual speech recognition, its multilingual translation, and its multilingual speech synthesis in regards to field experiments. We show the effects of system updates using the data collected from field experiments to improve our acoustic and language models.

Network-based mobile services, such as speech-to-speech translation and voice search, enable the construction of large-scale log database including speech. We have developed a smartphone application called VoiceTra for speech-to-speech translation and have collected 10,000,000 utterances so far. This huge corpus is unique in size and spatio-temporal information; it contains information on anonymized user locations. This spatiotemporal corpus can be used for improving the accuracy of its speech recognition and machine translation, and it will open the door for the study of the location dependency of vocabulary and new applications for location-based services. This paper first analyzes the corpus and then presents a novel method for classifying utterances using linguistic and non-linguistic information. L2-regularized Logistic Regression is used for utterance classification. Our experiments performed on the VoiceTra log corpus revealed that our proposed method outperformed baseline methods in terms of F measure.

In this paper we perform a comparison of lookahead composition and on-the-fly hypothesis rescoring using a common decoder. The results on a large vocabulary speech recognition task illustrate the differences in the behaviour of these algorithms in terms of error rate, real time factor, memory usage and internal statistics of the decoder. The evaluations were performed when the decoder was operated at either the state or arc level. The results show the dynamic approaches also work well at the state level even though there is greater dynamic construction cost.

This paper proposes a bootstrapping method of constructing a new spoken language understanding (SLU) system in a target language by utilizing statistical machine translation given an SLU module in some source language. The main challenge in this work is to induct unannotated automatic speech recognition results of user queries in the source language collected through a spoken dialog system, which is under public test. In order to select candidate expressions from among erroneous translation results stemming from problems with speech recognition and machine translation, we use back-translation results to check whether the translation result maintains the semantic meaning of the original sentence. We demonstrate that the proposed scheme can effectively prefer suitable sentences for inclusion in the training data as well as help improve the SLU module for the target language.

We propose a new method for automatically determining the smoothness of smooth classification error count loss for the recent Large Geometric Margin Minimum Classification Error (LGM-MCE) training. The method uses the Parzen-estimation-based formalization of MCE training, and it realizes the determination through the maximum likelihood estimation of error count risk in the one-dimensional geometric-margin-based misclassification measure. In the LGM-MCE framework, increase in the loss smoothness directly leads to an effect of producing virtual samples, which are expected to increase the training robustness to unseen samples. Focusing on this point, we also theoretically clarify the mechanism of this virtual sample generation. Through experiments, the utility of the proposed smoothness determination method is demonstrated, and the mechanism of producing virtual samples and its effect in robustness increase are also clearly illustrated.

Use of a linear projection (LP) function to transform multiple sets of acoustic models into a single set of acoustic models is proposed for characterizing testing environments for robust automatic speech recognition. The LP function is an extension of the linear regression (LR) function used in maximum likelihood linear regression (MLLR) and maximum a posteriori linear regression (MAPLR) by incorporating local information in the ensemble acoustic space to enhance the environment modeling capacity. To estimate the nuisance parameters of the LP function, we developed maximum likelihood LP (MLLP) and maximum a posteriori LP (MAPLP) and derived a set of integrated prior (IP) densities for MAPLP. The IP densities integrate multiple knowledge sources from the training set, previously seen speech data, current utterance, and a prepared tree structure. We evaluated the proposed MLLP and MAPLP on the Aurora-2 database in an unsupervised model adaptation manner. Experimental results show that the LP function outperforms the LR function with both ML- and MAP-based estimates over different test conditions. Moreover, because the MAP-based estimate can handle over-fittings well, MAPLP has clear improvements over MLLP. Compared to the baseline result, MAPLP provides a significant 10.99% word error rate reduction.

In this paper we describe a specialized Weighted Finite State Transducer (WFST) framework for handling class language models and dynamic vocabulary in automatic speech recognition. The proposed framework has several important features, a fused composition algorithm that substantially reduces the memory usage in comparison to generic WFST operations, and an efficient dynamic vocabulary scheme that allows for arbitrary new words to be added to class based language models on-thefly without requiring any changes to the pre-compiled transducers. The dynamic vocabulary approach achieves very low run-time costs by representing the dynamic vocabulary items inserted into the language model from an optimum set of existing lexicon items. Experimental results on a voice search task illustrate the low runtime costs of the proposed approach.

This work introduces a modified WFST-based mul-tiple to multiple EM-driven alignment algorithm for Grapheme-to-Phoneme (G2P) conversion, and pre-liminary experimental results applying a Recurrent Neural Network Language Model (RNNLM) as an N-best rescoring mechanism for G2P conversion. The alignment algorithm leverages the WFST framework and introduces several simple structural constraints which yield a small but consistent improvement in Word Accuracy (WA) on a selection of standard base-lines. The RNNLM rescoring further extends these gains and achieves state-of-the-art performance on four standard G2P datasets. The system is also shown to be significantly faster than existing solu-tions. Finally, the complete WFST-based G2P frame-work is provided as an open-source toolkit.

This paper addresses the problem of feature selection in the reinforcement learning (RL) of the dialog policies of spoken dialog systems. A statistical dialog manager selects the system actions the system should take based on the features derived from the current dialog state and/or the system's belief state. When defining the features used by the system for training the dialog policy, however, finding a set of actually effective features from potentially useful ones is not obvious. In addition, the selection should be done simultaneously with the optimization of the dialog policy. In this paper, we propose an incremental feature selection method for the optimization of a dialog policy by RL, in which improvement of the dialog policy and the feature selection are conducted simultaneously. Experiments in dialog policy optimization by RL with a user simulator demonstrated the following: 1) that the proposed method can find a better dialog policy with fewer policy iterations and 2) the learning speed is comparable with the case where feature selection is conducted in advance.

This paper presents an imitation learning method for object manipulation such as rotating an object or placing one object on another. In the proposed method, motions are learned using reference-point-dependent probabilistic models. Trajectory hidden Markov models (HMMs) are used as the probabilistic models so that smooth trajectories can be generated from the HMMs. The method was evaluated in physical experiments in terms of motion generation. In the experiments, a robot learned motions from observation, and it generated motions under different object placement. Experimental results showed that appropriate motions were generated even when the object placement was changed.

In an object manipulation dialogue, a robot may misunderstand an ambiguous command from a user, such as “Place the cup down (on the table),” potentially resulting in an accident. Although making confirmation questions before all motion will decrease the risk of this failure, the user will find it more convenient if confirmation questions are not made under trivial situations. This paper proposes a method for estimating ambiguity in the commands by introducing an active learning framework with Bayesian logistic regression to human-robot spoken dialogue. We conducted physical experiments in which a user and a manipulator-based robot communicated in spoken language to manipulate toys.

This paper addresses a user model and dialogue state definition in spoken consulting dialogue systems that help users in making decision. When selecting from a set of alternatives, users have various decision criteria for making decision. Users often do not have a definite goal or criteria for selection, and thus they may find not only what kind of information the system can provide but their own preference or factors that they should emphasize. In this paper, we model such consulting dialogue as partially observable Markov decision process (POMDP). We then present an optimization of dialogue strategy to help users make better decisions.

We proposed a dialog system using a weighted finite-state transducer (WFST) in which user concept and system action tags are input and output of the transducer, respectively. The WFST-based platform for dialog management enables us to combine various statistical models for dialog management (DM), user input understanding and system action generation, and then search the best system action in response to user inputs among multiple hypotheses. To test the potential of the WFST-based DM platform using statistical models, we constructed a dialog system using a human-to-human spoken dialog corpus for hotel reservation, which is annotated with Interchange Format (IF). A scenario WFST and a spoken language understanding (SLU) WFST were obtained from the corpus and then composed together and optimized. We evaluated the detection accuracy of the system next action tags using Mean Reciprocal Ranking (MRR). Finally, we constructed a full WFST-based dialog system by composing SLU, scenario and sentence generation (SG) WFSTs. Humans read the system responses in natural language and judged the quality of the responses. We confirmed that the WFST-based DM platform was capable of handling various spoken language and scenarios when the user concept and system action tags are consistent and distinguishable.

We have proposed an expandable dialog scenario description and platform to manage dialog systems using a weighted finite-state transducer (WFST) in which user concept and system action tags are input and output of the transducer, respectively. In this paper, we apply this framework to statistical dialog management in which a dialog strategy is acquired from a corpus of human-to-human conversation for hotel reservation. A scenario WFST for dialog management was automatically created from an N-gram model of a tag sequence that was annotated in the corpus with Interchange Format (IF). Additionally, a word-to-concept WFST for spoken language understanding (SLU) was obtained from the same corpus. The acquired scenario WFST and SLU WFST were composed together and then optimized. We evaluated the proposed WFST-based statistic dialog management in terms of correctness to detect the next system actions and have confirmed the automatically acquired dialog scenario from a corpus can manage dialog reasonably on the WFST-based dialog management platform.

Our goal in this study is to train a dialogue manager that can handle consulting dialogues through spontaneous interactions from a tagged dialogue corpus. We have collected 130 hours of consulting dialogues in sightseeing guidance domain. This paper provides our taxonomy of dialogue act (DA) annotation that can describe two aspects of utterances. One is a communicative function (speech act), and the other is a semantic content of an utterance. We provide an overview of the Kyoto tour guide dialogue corpus and a preliminary analysis using the dialogue act tags.

This paper proposes a method that generates motions and utterances in an object manipulation dialogue task. The proposed method integrates belief modules for speech, vision, and motions into a probabilistic framework so that a user’s utterances can be understood based on multimodal information. Responses to the utterances are optimized based on an integrated confidence measure function for the integrated belief modules. Bayesian logistic regression is used for the learning of the confidence measure function. The experimental results revealed that the proposed method reduced the failure rate from 12% down to 2.6% while the rejection rate was less than 24%. Index Terms: multimodal spoken dialogue system, robot language acquisition, confidence, Bayesian logistic regression

In order to build accurate target profiles, most definition question answering (QA) systems primarily involve utilizing various external resources, such as WordNet, Wikipedia, Biograpy.com, etc. However, these external resources are not always available or helpful when answering definition questions. In contrast, this paper proposes an unsupervised classification model, called the U-Model, which can liberate definitional QA systems from heavily depending on a variety of external resources via applying sentence expansion ($SE$) and SVM classifier. Experimental results from testing on English TREC test sets reveal that the proposed U-Model can not only significantly outperform baseline system but also require no specific external resources.

This paper described our development dialog system on Kyoto tourist information assistance. Dialog part of our system helped user to make an appropriate query. Information analysis part would be assisted for user to select the retrieved information. Nowadays we can get most information through the Internet. However, we have a trouble to pick up expected information from the huge results with conventional search engines. Especially in mobile terminal, we are confronted with great difficulties for two factors. One is that most of users cannot make an appropriate query because their request is vague with theirselves. The other is that the retrieved information has huge variation and mobile terminal has small area for displaying them. Therefore, we aim to develop technologies for the users to input their requests by familiar way and clarify what they want to know with displaying the retrieved information with suitable method.

Conventional question answering (QA) techniques independently process candidate-bearing snippets to select an exact answer to a question from candidate answers. This paper presents two novel ways of utilizing redundancy in candidate-bearing snippets to help select an exact answer to a question in our Web QA system, i.e., cluster-based language model (CLM-M) and unsupervised SVM classifier (U-SVM) techniques. The comparative experiments demonstrate that the proposed methods significantly outperform the language model-based (LM-M) and supervised SVM-based (S-SVM) techniques that do not utilize this redundancy in the candidate-bearing snippets. Using the CLM-M, the top_1 score is increased from 36.03% (LM-M) to 46.96%; and the top_1 improvement in the U-SVM over the S-SVM is about 23%. Moreover, a cross-model comparison shows that the performance ranking of these models is: U-SVM > CLM-LM > LM-M > S-SVM > R-M (the retrieval-based model).

Spoken monologues feature greater sentence length and structural complexity than do spoken dialogues. To achieve high parsing performance for spoken monologues, it could prove effective to simplify the structure by dividing a sentence into suitable language units. This paper proposes a method for dependency parsing of Japanese monologues based on sentence segmentation. In this method, the dependency parsing is executed in two stages: at the clause level and the sentence level. First, the dependencies within a clause are identified by dividing a sentence into clauses and executing stochastic dependency parsing for each clause. Next, the dependencies over clause boundaries are identified stochastically, and the dependency structure of the entire sentence is thus completed. An experiment using a spoken monologue corpus shows this method to be effective for efficient dependency parsing of Japanese monologue sentences.

The conversion method from Non-Audible Murmur (NAM) to ordinary speech based on the statistical voice conversion (NAM-to-Speech) has been proposed towards realization of "silent speech telephone." Although NAM-to-Speech converts NAM to intelligible voices with similar quality to speech, there is still a large problem, i.e., difficulties of the F0 estimation from unvoiced speech. In order to avoid this problem, we propose a conversion method from NAM to whisper that is a familiar and intelligible unvoiced speech (NAM-to-Whisper). Moreover, we enhance NAM-to-Whisper so that multiple types of body-transmitted unvoiced speech such as NAM and Body Transmitted Whisper (BTW) are accepted as input voices. We evaluate the performance of the proposed conversion method. Experimental results demonstrate that 1) intelligibility and naturalness of NAM are significantly improved by NAM-to-Whisper, 2) NAM-to-Whisper outperforms NAM-to-Speech, and 3) we can train a single conversion model successfully converting both NAM and BTW to the target voice.

We developed the next generation of skin-attachment sensors for sampling NAM (Non-Audible Murmur) signals by using soft silicone, which has an acoustic impedance close to that of human flesh, as the prime medium of vibration. With new NAM microphones we could sample expanded target voice signal, suppressing air conduction noise signal to low by the experiment of synchronous stereo sampling of air and flesh conduction voices at the same gain. The bandwidth of the soft silicone type NAM microphone has improved and we obtain a much higher accuracy of both NAM and BTOS (Body Transmitted Ordinary Speech) recognition compared with the stethoscopic type. Aural comprehension test showed that accuracy of catching sentences of NAM and BTOS has improved with soft silicon type NAM microphone almost as high as that of air conduction voices. However, the extremely low accuracy of meaningless words is a problem to be solved for developing a "Non-Voice Phone".

In applications of spoken monologue processing such as simultaneous machine interpretation and real-time captions generation, incremental language parsing is strongly required. This paper proposes a technique for incremental dependency parsing of Japanese spoken monologue on a clause-by-clause basis. The technique identifies the clauses based on clause boundaries analysis, analyzes the dependency structures of them, and tries to decide the dependency relations with another clauses, simultaneously with the monologue speech input. The dependency relations are generated at the stage before the input of the entire monologue, and therefore, our technique can be used for language parsing in simultaneous Japanese speech understanding. An experiment using Japanese monologues has shown that our technique had the same degree of the performance as the usual dependency parsing for monologue sentences.

Example-based machine translation (EBMT) systems, so far, rely on heuristic measures in retrieving translation examples. Such a heuristic measure costs time to adjust, and might make its algorithm unclear. This paper presents a probabilistic model for EBMT. Under the proposed model, the system searches the translation example combination which has the highest probability. The proposed model clearly formalizes EBMT process. In addition, the model can naturally incorporate the context similarity of translation examples. The experimental results demonstrate that the proposed model has a slightly better translation quality than state-of-the-art EBMT systems.

We propose a novel method for estimating speech rate based on the durations of similar patterns as a first step in determining the relation between various speaking styles used in everyday conversation and speaker intentions or attitudes. Whereas most methods of determining speaking rate require manually obtained label information or linguistic knowledge, the proposed method uses patterns of speech-sound sequences that occur relatively frequently in dialogue speech, as detected from the speech waveform information alone. For use as an index of speaking rate, the method calculates the z-score of each pattern duration, relative to the distribution of the respective pattern groups. The method uses speech recognition to provide a rough classification of the speech sounds, i.e., as a phonetic typewriter, but without requiring accuracy of recognition in any meaningful linguistic terms. From a large body of natural dialogue speech data, it divides the label sequences obtained from the recognizer/classifier into variable length patterns according to maximum likelihood, and classifies all speech segments having the same pattern as a group. The validity of speech rate detection was evaluated.

We propose a new style of practical input interface for the recognition of non-audible murmur (NAM), i.e., for the recognition of inaudible speech produced without vibration of the vocal folds. We have developed a microphone attachment, which adheres to the skin, applying the principle of a medical stethoscope, found the ideal position for sampling flesh-conducted NAM sound vibration and retrained an acoustic model with NAM samples. Then, using the Julius Japanese Dictation Toolkit, we tested the possibilities for practical use of this method in place of an external microphone for analyzing air-conducted voice sound.

We propose a new style of practical input interface for the recognition of non-audible murmur (NAM), i.e., for the recognition of inaudible speech produced without vibration of the vocal folds. We developed a microphone attachment, which adheres to the skin, applying the principle of a medical stethoscope, found the ideal position for sampling flesh-conducted NAM sound vibration and retrained an acoustic model with NAM samples. Then using the Julius Japanese Dictation Toolkit, we tested the possibilities for practical use of this method in place of an external microphone for analyzing air-conducted voice sound. Additionally we propose laryngeal elevation index (LEI), a new index of prosody, which can show the prosody of NAM without F0, using simple processing of images from medical ultrasonography. We realized and defined NAM never used for input or communication and propose that we should make use of it for the interface of human-human and human-cybernetic machines.

Many studies have been reported in the domain of speech-to-speech machine translation systems for travel conversation use. Therefore, a large number of travel domain corpora have become available in recent years. From a wider viewpoint, speech-to-speech systems are required for many purposes other than travel conversation. One of these is monologues (e.g., TV news, lectures, technical presentations). However, in monologues, sentences tend to be long and complicated, which often causes problems for parsing and translation. Therefore, we need a suitable translation unit, rather than the sentence. We propose the clause as a unit for translation. To develop a speech-to-speech machine translation system for monologues based on the clause as the translation unit, we need a monologue parallel corpus with clause alignment. In this paper, we describe how to build a Japanese-English monologue parallel corpus with clauses aligned, and discuss the features of this corpus.

Cross-language voice conversion is useful for many applications, and we are trying to apply the technique to a language training system for reducing voice individuality differences. In this paper, we describe experiments that test effectiveness of an extension of singlelanguage voice conversion, to include cross-language utterances. The performance was investigated by objective and perceptual evaluation using bilingual-speakers data for training. Then, the correlations between a computed distance measure and a human perceptual pronunciation evaluation score were compared before and after applying conversion. From these results, it was found that the crosslanguage voice conversion reduces speakers' voice differences between the pairs, and the phoneme based measures show somewhat clearer correspondences to the human perceptual score in vowels' test after applying voice conversion.

Evaluation of machine translation is one of the most important issues in this field. We have already proposed a quantitative evaluation of machine translation system. The method was roughly that an example sentence in Japanese is machine translated into English, and then into Japanese using several systems, and that the comparison of output Japanese sentences with the original Japanese sentence is done for the word identification, the correctness of the modification, the syntactic dependency, and the parataxis. By calculating the score, we could quantitatively evaluate the English machine translation. However, the extraction of word identification etc. was done by human, and the fact affects the correctness of evaluation. In order to solve this problem, we developed an automatic evaluation system. We report the detail of the system in this paper..

In this paper, we discuss applying a translation model, “Transfer Driven Machine Translation” (TDMT), to document abstracts on science and technology. TDMT, a machine translation model, was developed by ATR-ITL to deal with dialogues in the travel domain. ATR-ITL has reported that the TDMT system efficiently translates multi-lingual spoken-dialogs. However, little is known about the ability of TDMT to translate written text translations; therefore, we examined TDMT with written text from English to Japanese, especially abstracts on science and technology produced by the Japan Science and Technology Corporation (JST). The experimental results show that TDMT can derive written text translation.

Compared with off-line machine translation (MT). MT for the WWW has more evaluation factors such as translation accuracy of text, interpretation of HTML tags, consistency with various protocols and browsers, and translation speed for net surfing. Moreover, the speed of technical innovation and its practical application is fast, including the appearance of new protocols. Improvement of MT software for the WWW will enable the sharing of information from around the world and make a great deal of contribution to mankind. Despite the importance of general evaluation studies on MT software for the WWW. it appears that such studies have not yet been conducted. Since MT for the WWW will be a critical factor for future international communication, its study and evaluation is an important theme. This study aims at standardized evaluation of MT for the WWW. and suggests an evaluation method focusing on unique aspects of the WWW independent of text. This evaluation method has a wide range of aptitude without depending on specific languages. Twenty-four items specific to the WWW were actually evaluated with regard to six MT software for the WWW. This study clarified various issues which should be improved in the future regarding MT software for the WWW and issues on evaluation technology of MT on the Internet.

ATR has built a multi-language speech translation system called ATR-MATRIX. It consists of a spoken-language translation subsystem, which is the focus of this paper, together with a highly accurate speech recognition subsystem and a high-definition speech synthesis subsystem. This paper gives a road map of solutions to the problems inherent in spoken-language translation. Spoken-language translation systems need to tackle difficult problems such as ungrammaticality. contextual phenomena, speech recognition errors, and the high-speeds required for real-time use. We have made great strides towards solving these problems in recent years. Our approach mainly uses an example-based translation model called TDMT. We have added the use of extra-linguistic information, a decision tree learning mechanism, and methods dealing with recognition errors.

One of the most important issues in the field of machine translation is evaluation of the translated sentences. This paper proposes a quantitative method of evaluation for machine translation systems. The method is as follows. First, an example sentence in Japanese is machine translated into English using several Japanese-English machine translation systems. Second, the output English sentences are machine translated into Japanese using several English-Japanese machine translation systems (different from the Japanese-English machine translation systems). Then, each output Japanese sentence is compared with the original Japanese sentence in terms of word identification, correctness of the modification, syntactic dependency, and parataxes. An average score is calculated, and this becomes the total evaluation of the machine translation of the sentence. From this two-way machine translation and the calculation of the score, we can quantitatively evaluate the English machine translation. For the present study, we selected 100 Japanese sentences from the abstracts of scientific articles. Each of these sentences has an English translation which was performed by a human. Approximately half of these sentences are evaluated and the results are given. In addition, a comparison of human and machine translations is also performed and the trade-off between the two methods of translation is discussed.