activity
20232026
collaborators

11 papers

cs.CL2026

BaLEEN: Biasing with Latent Encoded Entities for Context-Aware ASR

Chihiro Taguchi, Yotaro Kubo, Rujikorn Charakorn

Transcribing domain-specific entities and rare proper nouns remains a major challenge in automatic speech recognition (ASR). In this paper, we propose BaLEEN (Biasing with Latent E…

cs.CL2026

Pretrained self-supervised speech models can recognize unseen consonants

Chihiro Taguchi, Éric Le Ferrand, Hirosi Nakagawa +4

Modern pretrained self-supervised automatic speech recognition models are trained on large-scale audio data to encode speech into contextualized representations. However, their tra…

cs.CL2026

Automatic Speech Recognition for Documenting Endangered Languages: Case Study of Ikema Miyakoan

Chihiro Taguchi, Yukinori Takubo, David Chiang

Language endangerment poses a major challenge to linguistic diversity worldwide, and technological advances have opened new avenues for documentation and revitalization. Among thes…

cs.CL2025

Creating ConLangs to Probe the Metalinguistic Grammatical Knowledge of LLMs

Chihiro Taguchi, Richard Sproat

We present a system that uses LLMs as a tool in the development of Constructed Languages -- ConLangs, which we call IASC (Interactive Agentic System for ConLangs). The system is mo…

cs.CL2025

Building Tailored Speech Recognizers for Japanese Speaking Assessment

Yotaro Kubo, Richard Sproat, Chihiro Taguchi +1

This paper presents methods for building speech recognizers tailored for Japanese speaking assessment tasks. Specifically, we build a speech recognizer that outputs phonemic labels…

cs.CL2025

Languages Still Left Behind: Toward a Better Multilingual Machine Translation Benchmark

Chihiro Taguchi, Seng Mai, Keita Kurabe +4

Multilingual machine translation (MT) benchmarks play a central role in evaluating the capabilities of modern MT systems. Among them, the FLORES+ benchmark is widely used, offering…

cs.CL2025

Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-

Chihiro Taguchi, Seiji Maekawa, Nikita Bhutani

Retrieval-augmented generation (RAG) and long-context language models (LCLMs) both address context limitations of LLMs in open-domain question answering (QA). However, optimal exte…

cs.CL2025

SoftMatcha: A Soft and Fast Pattern Matcher for Billion-Scale Corpus Searches

Hiroyuki Deguchi, Go Kamoda, Yusuke Matsushita +4

Researchers and practitioners in natural language processing and computational linguistics frequently observe and analyze the real language usage in large-scale corpora. For that p…

cs.CL2024

Language Complexity and Speech Recognition Accuracy: Orthographic Complexity Hurts, Phonological Complexity Doesn't

Chihiro Taguchi, David Chiang

We investigate what linguistic factors affect the performance of Automatic Speech Recognition (ASR) models. We hypothesize that orthographic and phonological complexities both degr…

cs.CL2024

Killkan: The Automatic Speech Recognition Dataset for Kichwa with Morphosyntactic Information

Chihiro Taguchi, Jefferson Saransig, Dayana Velásquez +1

This paper presents Killkan, the first dataset for automatic speech recognition (ASR) in the Kichwa language, an indigenous language of Ecuador. Kichwa is an extremely low-resource…

cs.CL2023

Universal Automatic Phonetic Transcription into the International Phonetic Alphabet

Chihiro Taguchi, Yusuke Sakai, Parisa Haghani +1

This paper presents a state-of-the-art model for transcribing speech in any language into the International Phonetic Alphabet (IPA). Transcription of spoken languages into IPA is a…