In Japan, handwriting practice is an important part of education and often involves repeatedly writing the same character, which can become monotonous and reduce motivation. We propose a melody-based handwriting system that maps pen strokes to pitches and stops playback when the stroke leaves a predefined spatial gate, providing spatially-contingent auditory feedback that encourages accurate tracing while maintaining engagement. We implemented a browser-native, low-latency prototype that synchronizes visual input and audio in real time. In a controlled experiment comparing continuous feedback, restricted feedback (proposed), and no feedback, the proposed method led to slower, more attentive writing and a higher proportion of strokes within the designated range, indicating improved precision.

Kento Watanabe, Masataka Goto. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Abstract This paper explores the effectiveness of a flexible song recommendation function implemented in a music web service. The function allows users to create recommendation models, which we refer to as intent-based recommendation models (IBRMs), according to their intents. For example, a user can develop IBRMs for “cool songs,” “songs for concentrating on work,” and so on, and receive recommendations from each of the IBRMs according to her intents. The key novelty of this work lies in the architecture that enables users to explicitly construct and maintain multiple personalized recommendation models in parallel, each specialized for a particular intent. This user-driven approach contrasts with conventional systems that rely on a single, system-controlled recommendation model per user. To develop an IBRM, the user first initializes it by choosing seed songs and then repeatedly updates it by giving feedback based on whether recommended songs are relevant to the user’s intent. In the case study using the real-world web service “Kiite,” we analyze 1,116 IBRMs created by 417 users and show key characteristics of those IBRMs (e.g., it is meaningful to enable users to create their own IBRMs, because the created IBRMs generate largely different recommendation results from one another). These findings demonstrate the effectiveness and practical value of enabling users to control intent-specific recommendation behavior through the proposed IBRM framework.

This study introduces self-supervised contrastive learning to acquire feature representations of singing voices. To acquire robust representations in an unsupervised manner, regular self-supervised contrastive learning trains neural networks to make the feature representation of a sample close to those of its computationally transformed versions. Similarly, we employ two transformations—pitch shifting and time stretching—considering the nature of singing voices. Nevertheless, we use them reversely: we train networks to push away representations of the transformed versions. The networks then attempt to discriminate changes in vocal timbres introduced by pitch shifting without time stretching and those in singing expressions introduced by time stretching without pitch shifting. Consequently, the acquired representations become attentive to vocal timbre and singing expression. This was confirmed through a singer identification task, where we trained a classifier to learn the relationship between the feature representations to the corresponding singer labels of 500 singers. As a result, the employed transformations helped the classifier improve the classification accuracy by 9.12% (top-1 accuracy: 63.08%) compared with the case where the feature representations fed to the classifier were acquired without the transformations (top-1 accuracy: 53.96%). Furthermore, the proposed approach can be extended to acquire feature representations attentive to either vocal timbre or singing expression but not to the other by changing how the transformations are incorporated. We particularly explored the characteristics of such vocal timbre- or singing expression-oriented feature representations against song genre, singer gender, and vocal technique, and confirmed that they successfully capture different aspects of singing voices.

Kento Watanabe, Yuichiroh Matsubayashi, Satoru Fukayama, Masataka Goto, Kentaro Inui, Tomoyasu Nakano. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

This paper presents LyriSys, a novel lyric-writing support system. Previous systems for lyric writing can fully automatically only generate a single line of lyrics that satisfies given constraints on accent and syllable patterns or an entire lyric. In contrast to such systems, LyriSys allows users to create and revise their work incrementally in a trial-and-error manner. Through fine-grained interactions with the system, the user can create the specifications of the musical structure and the story of the lyrics in terms of the verse-bridge-chorus structure, the number of lines, words and syllables, and most importantly, the transition over semantic topics such as "scene", "dark" and "sweet love". This paper provides an overview of the design of the system and its user interface and describes how the writing process is guided by a state-of-the-art probabilistic generative topic model that is trained without supervision. The system works for both Japanese and English.

This study proposes a computational model of the discourse segments in lyrics to understand and to model the structure of lyrics. To test our hypothesis that discourse segmentations in lyrics strongly correlate with repeated patterns, we conduct the first large-scale corpus study on discourse segments in lyrics. Next, we propose the task to automatically identify segment boundaries in lyrics and train a logistic regression model for the task with the repeated pattern and textual features. The results of our empirical experiments illustrate the significance of capturing repeated patterns in predicting the boundaries of discourse segments in lyrics.

In this paper, bilateral control with two degrees of freedom (2-DOF) haptic spherical interface is proposed. There is much research about the mechanical structure of the haptic interface to realize large movable range. However, these are not suitable for the interface of the open environment. In addition, to achieve wide movable range, there are some problems that the robot gets large and the large inertia deteriorates the operationality according to the movable range. In this paper, by utilizing a sphere as a interface, the interface has the two degrees of freedom and the unlimited movable range. The interface is utilized as the master robot. Meanwhile, an X-Y stage is utilized as the slave robot. The number of the actuators, the actuator type, and the structures are different between master-slave robots. Hence, bilateral control considering the differences is proposed.