Takashi Nose

Cyclooxygenase inhibitors augment the production of pro-matrix metalloproteinase 9 (progelatinase B) in rabbit articular chondrocytes

Matrix metalloproteinase 9 (MMP‐9/gelatinase B) has recently been proposed to participate in the destruction of articular cartilage. Here, we report that interleukin 1 (IL‐1) enhances the production of the precursor of MMP‐9 in rabbit articular chondrocytes in primary culture, and this IL‐1‐mediated production of proMMP‐9 is greatly augmented by cyclooxygenase inhibitors such as diclofenac and indomethacin, whereas the constitutive production of proMMP‐2 (progelatinase A) is not modulated by IL‐1 and/or cyclooxygenase inhibitors. Exogenous prostaglandin (PG) E1 and PGE2 suppress the proMMP‐9 production in a dose‐dependent manner. Similar results are also obtained with cultured rabbit synoviocytes. These results provide the first evidence that PGE down‐regulates the production of proMMP‐9 in chondrocytes and synoviocytes. Thus, cyclooxygenase inhibitors probably exert undesirable catabolic actions on the maintenance of articular cartilage under inflammatory conditions.

Sentence Selection Based on Extended Entropy Using Phonetic and Prosodic Contexts for Statistical Parametric Speech Synthesis

This paper proposes a sentence selection technique for constructing phonetically and prosodically balanced compact recording scripts for speech synthesis. In the conventional corpus design of speech synthesis, a greedy algorithm that maximizes phonetic coverage is often used. However, for statistical parametric speech synthesis, balances of multiple phonetic and prosodic contextual factors are important as well as the coverage. To take account of both of the phonetic and prosodic contextual balances in sentence selection, we introduce an extended entropy of phonetic and prosodic contexts, such as biphone/triphone, accent/stress/tone, and sentence length. For detailed investigation, conventional and proposed techniques are evaluated using Japanese, English, and Chinese corpora. The objective experimental results show that the proposed technique achieves better coverage and balance of contexts. In addition, speech synthesis experiments based on hidden Markov models reveal that the generated speech parameters become closer to those of the natural speech compared with other conventional sentence selection techniques. Subjective evaluations show that the proposed sentence selection based on the extended entropy improves the naturalness of the synthetic speech while maintaining the similarity to the original sample.

Collection of Example Sentences for Non-task-Oriented Dialog Using a Spoken Dialog System and Comparison with Hand-Crafted DB.

Designing a question-answer database is important to make natural conversation for an example-based dialog system. We focused on the method to collect the example sentences by actual conversations with the system. In this study, examples in the database were collected from the conversation logs, then we investigated the relationship between the response accuracy and the number of the interaction. In the experiment, the transcriptions of the user’s utterances are added to the database at every end of the interaction. The responce sentences in the database were created manually. The result showed that the response accuracy appropriateness improved as increasing the number of the interactions and saturated at around 70%. In addition, we compared the collected database with the fully handcrafted database by the subjective evaluation. The score of the user satisfaction, dialog engagement, intelligence, and willingness to use were higher than the handcrafted database, and these results suggested that the proposed method can obtain more appropriate examples to the actual conversation from subjective point of view.

Detection of Singing Mistakes from Singing Voice

We investigate a method of detecting the wrong lyrics from the singing voice. In the proposed method, we compare the input singing voice and the reference singing voice using dynamic time warping, and then observe the frame-by-frame distance to find the error location. However, the absolute value of the distance is affected by the singer individuality of the reference and input singing voice. Thus, we attempted to adapt the singer individuality into the reference singer’s one by a linear transformation. The results of the experiment showed that we could detect the wrong lyrics with high accuracy when the different part of the lyrics was long. In addition, we investigated the effect of iterative linear transformation, and we could not find any benefit from the second or third linear transformations.

Response Selection of Interview-Based Dialog System Using User Focus and Semantic Orientation

This research examined the response selection method of an interview-based dialog system that obtains the user’s information by the chat-like conversation. In the interview dialog, the system should ask about the subject that the user is interested in to obtain the user’s information efficiently. In this paper, we proposed the method to select the system’s utterance based on the user’s emotion to a focus detected from the user’s utterance. We prepared the question types corresponding to the semantic orientation, such as the positive, neutral, and negative. The focus was detected by the CRF, and the question type was estimated from the user’s utterance and the system’s previous utterance.

A Study on 2D Photo-Realistic Facial Animation Generation Using 3D Facial Feature Points and Deep Neural Networks

This paper proposes a technique for generating a 2D photo-realistic facial animation from an input text. The technique is based on the mapping from 3D facial feature points with deep neural networks (DNNs). Our previous approach was based only on a 2D space using hidden Markov models (HMMs) and DNNs. However, this approach has a disadvantage that generated 2D facial pixels are sensitive to the rotation of the face in the training data. In this study, we alleviate the problem using 3D facial feature points obtained by Kinect. The information of the face shape and color is parameterized by the 3D facial feature points. The relation between the labels from texts and face-model parameters are modeled by DNNs in the model training. As a preliminary experiment, we show that the proposed technique can generate the 2D facial animation from arbitrary input texts.

Dialog-Based Interactive Movie Recommendation: Comparison of Dialog Strategies

The user interface based on natural language dialog has been gathering attention. In this paper, we focus on the dialog-based user interface of movie recommendation system. We compared two kinds of dialog systems: the system-initiative system presented all the information about the recommended item at a time, and the user-initiative system provided information of the recommended item based on a dialog between the system and the user. As a result of dialog experiment, the users preferred to the user-initiative system for availability of obtaining required information, while the system-initiative system was chosen for the simplicity of obtaining the information. In addition, it was found that the appropriateness of the system’s replies in the dialog affected the user’s preference to the user-initiative system.

Evaluation of Nonlinear Tempo Modification Methods Based on Sinusoidal Modeling

Modifying tempo of musical signal is one of the basic signal processing for music signal, and many methods have been proposed so far. Nishino et al. proposed a tempo modification method of nonlinear modification based on sinusoidal model, but the evaluation of the methods was insufficient. In this paper, we evaluated the tempo modification methods with sinusoidal model and nonlinear signal stretch and compression. Namely, we compared effectiveness of use of residue signal and methods of determination of stretchable parts. From the experimental result, we could confirm the efficiency of the nonlinear tempo modification. We also compared several methods of determining the stretchable parts as well as the use of residue signal. As a result, the effect of the methods depended on the input signal.

Voice Conversion from Arbitrary Speakers Based on Deep Neural Networks with Adversarial Learning

In this study, we propose a voice conversion technique from arbitrary speakers based on deep neural networks using adversarial learning, which is realized by introducing adversarial learning to the conventional voice conversion. Adversarial learning is expected to enable us more natural voice conversion by using a discriminative model which classifies input speech to natural speech or converted speech in addition to a generative model. Experiments showed that proposed method was effective to enhance global variance (GV) of mel-cepstrum but naturalness of converted speech was a little lower than speech using the conventional variance compensation technique.

Development and Evaluation of Julius-Compatible Interface for Kaldi ASR

In recent years, the use of Kaldi has rapidly grown because it has adopted various technologies of DNN-based speech recognition in succession and has shown high recognition performance. On the other hand, the speech recognition engine, Julius, has been widely used especially in Japan. Julius is also attracting attention since DNN-HMM is implemented in it. In this paper, we describe the design plan of interfaces that make Kaldi speech recognition engine be compatible with Julius, a system overview, and the details of the speech input unit and the recognition result output unit. We also refer to the functions that we are planning to implement.