arXiv subjects

Shigeichi Hirasawa

Publications and source records attributed to Shigeichi Hirasawa.

5 recordsLinked to original sources

arXiv2026

A Semi-Automated System for Generating Dialogue-Based TTS Lessons Using Large Language Models: An Exploratory Study of Educational Potential

This study proposes a semi-automated system for generating dialogue-based lessons using Large Language Models (LLMs) and Text-to-Speech (TTS) technology, and exploratorily examines its educational potential via a practical quasi-experiment. The system augments rather than replaces educators through a three-stage human-in-the-loop workflow (LLM-based slide/narration generation, educator review, automated audiovisual integration), and introduces a novel method for generating Expert-Novice dialogue narration based on cognitive apprenticeship theory. In a study of 245 first-year high school students who sequentially experienced three lesson formats (instructor voice, single-speaker TTS, dialogue TTS; content differed across sessions, limiting format/content separation), we conducted within-subject (Friedman test, N<=183) and repeated cross-sectional (Mann-Whitney U, N=229/206) analyses. TTS audio did not substantially degrade the learning experience versus instructor voice, supported by TOST equivalence testing. Dialogue TTS was significantly superior to single TTS in comprehension (p=.006, q=.025) and cognitive engagement (p=.019, q=.048); enjoyment was non-significant after FDR correction (q=.081) but reached significance after controlling for prior knowledge (proportional-odds model, OR=1.65, q=.025), and these advantages were not attributable to prior-knowledge imbalance. Conversely, single TTS was superior in audio naturalness (p<.001, q<.001, r=-.238), revealing a trade-off between dialogue's benefits and higher extraneous cognitive load. Dialogue format was preferred by 66.9% of learners as most enjoyable (p<.001). These results reflect a fixed-order design; replication is needed before generalizing them as effects of lesson format. This study provides a theoretical and empirical basis for the educational acceptability of TTS audio and for TTS lesson-format design.

cs.CY↗

arXiv2026

Interaction Effects Between Learner Characteristics and Dialogue Format in TTS Dialogue-Based Lessons

This study examined how learner characteristics affect motivation, learning outcomes, and overall evaluation in three types of dialogue-based lessons---(1) teacher--student, (2) student--student, and (3) teacher--teacher---generated using a large language model (LLM) and Text-to-Speech (TTS) technology. In particular, we focused on the interaction effects between dialogue format and learners' experiential learning style (the Concrete Experience factor, CE; and the factor of active experimentation through reflective observation and abstract conceptualization, RCE) and critical thinking disposition. Using a repeated-measures design with 222 first-year high school students, we analyzed the data with linear mixed-effects models. The results showed a significant interaction between learner characteristics and dialogue format for ARCS-based motivation. Specifically, the effect of the CE factor on motivation was more strongly positive in the teacher--teacher format than in the teacher--student format, whereas the positive effect of the RCE factor was relatively weaker in the teacher--teacher format. For learning outcomes, the interactions between dialogue format and both the CE and RCE factors showed a trend toward significance. No significant interaction emerged for overall evaluation; however, the overall evaluation of the teacher--teacher format was significantly lower than that of the teacher--student format, a pattern that diverged from the positive effect observed for motivation. These results suggest that dialogue format should be selected according to learner characteristics in TTS dialogue-based lessons. Because the effect sizes of the significant interactions were all small to medium, however, the findings of this study should be regarded as preliminary evidence for the design of personalized learning.

cs.CY↗

arXiv2015

Linear Programming Decoding of Binary Linear Codes for Symbol-Pair Read Channels

In this paper, we develop a new decoding algorithm of a binary linear codes for symbol-pair read channels. Symbol-pair read channel has recently been introduced by Cassuto and Blaum to model channels with high write resolution but low read resolution. The proposed decoding algorithm is based on a linear programming (LP). It is proved that the proposed LP decoder has the maximum-likelihood (ML) certificate property, i.e., the output of the decoder is guaranteed to be the ML codeword when it is integral. We also introduce the fractional pair distance $d_{fp}$ of a code which is a lower bound on the pair distance. It is proved that the proposed LP decoder will correct up to $\lceil d_{fp}/2\rceil-1$ pair errors.

cs.IT↗

arXiv2014

Constructions of A Large Class of Optimum Constant Weight Codes over F_2

A new method of constructing optimum constant weight codes over F_2 based on a generalized $(u, u+v)$ construction is presented. We present a new method of constructing superimposed code $C_{(s_1,s_2,\cdots,s_I)}^{(h_1, h_2, \cdots, h_I)}$ bound. and presented a large class of optimum constant weight codes over F_2 that meet the bound due to Brouwer and Verhoeff, which will be referred to as BV . We present large classes of optimum constant weight codes over F_2 for $k=2$ and $k=3$ for $n \leqq 128$. We also present optimum constant weight codes over F_2 that meet the BV bound for $k=2,3,4,5$ and 6, for $n \leqq 128$. The authors would like to present the following conjectures : $C_{I}$: $C_{(s_1)}^{(h_1)}$ presented in this paper yields the optimum constant weight codes for the code-length $n=3h_1$, number of information symbols $k=2$ and minimum distance $d=2h_1$ for any positive integer $h_1$. $C_{II}$: $C_{(s_1)}^{(h_1)}$ yields the optimum constant weight codes at $n=7h_1, k=3$ and $d=4h_1$ for any $h_1$. $C_{III}$: Code $C_{(s_1,s_2,\cdots,s_I)}^{(h_1, h_2, \cdots, h_I)}$ yields the optimum constant weight codes of length $n=2^{k+1}-2$, and minimum distance $d=2^{k}$ for any number of information symbols $k\geq 3$.

cs.IT↗

arXiv2009

Bayesian Forecasting of WWW Traffic on the Time Varying Poisson Model

Traffic forecasting from past observed traffic data with small calculation complexity is one of important problems for planning of servers and networks. Focusing on World Wide Web (WWW) traffic as fundamental investigation, this paper would deal with Bayesian forecasting of network traffic on the time varying Poisson model from a viewpoint from statistical decision theory. Under this model, we would show that the estimated forecasting value is obtained by simple arithmetic calculation and expresses real WWW traffic well from both theoretical and empirical points of view.

cs.NI↗