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

Chihiro Taguchi; Yukinori Takubo; David Chiang

cs.CLMar 27, 2026

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

Authors: Chihiro Taguchi, Yukinori Takubo, David Chiang

Organizations: University of Notre Dame, Notre Dame, IN, USA · National Institute for Japanese Language and Linguistics, Tokyo, Japan

Abstract

Language endangerment poses a major challenge to linguistic diversity worldwide, and technological advances have opened new avenues for documentation and revitalization. Among these, automatic speech recognition (ASR) has shown increasing potential to assist in the transcription of endangered language data. This study focuses on Ikema, a severely endangered Ryukyuan language spoken in Okinawa, Japan, with approximately 1,300 remaining speakers, most of whom are over 60 years old. We present an ongoing effort to develop an ASR system for Ikema based on field recordings. Specifically, we (1) construct a 6.33-hour speech corpus from field recordings, (2) train an ASR model that achieves a character error rate as low as 15%, and (3) evaluate the impact of ASR assistance on the efficiency of speech transcription. Our results demonstrate that ASR integration can substantially reduce transcription time and cognitive load, offering a practical pathway toward scalable, technology-supported documentation of endangered languages.

Figures & tables

Figure 2: Map of the Miyako Islands (bottom) in Japan (top). The Ikema-speaking villages are marked with a red circle.

Dataset	#Samples	Duration	Avg. duration
		(sec)	(sec)
Field	11126	16058	1.44
Dictionary	5680	5379	0.95
Audiobooks	285	1348	4.73
Total	17091	22785	1.33

Table 1: Statistics of the datasets.

Figure 3: Segmentation and annotation in ELAN. The top transcription tier contains pause-based segments, while the bottom tier contains longer segments that concatenate multiple pause-based segments to approximate sentence-level units.

	kana		romaji
Model	CER	WER	CER	WER
xls-r-300m	20.94	62.56	14.80	64.99
xls-r-1b	24.07	66.61	20.33	76.73
mms-1b	21.86	63.22	18.98	73.87

Table 2: ASR performances of the trained models.

Figure 4: The curves of the CERs on the validation data during the training.

	w/o ASR	w/ ASR	Speedup
Annotator A	11:36	9:43	+19.38%
Annotator B	30:49	25:00	+23.27%

Table 3: A comparison of annotation speed, with and without ASR output. The speedup is computed by dividing the time without ASR by the time with ASR.

Recording ID	Style	Duration	#Samples	#Kana	#Words
I0482_usI	Spontaneous	111.03	69	1105	202
I0482_ngi	Spontaneous	100.36	72	885	201
I0482_pic_mucIusa	Spontaneous	85.93	56	683	132
I0482_pic_buugii	Spontaneous	72.91	49	534	117
I0482_byuuigassa	Spontaneous	53.51	35	454	98
I0482_bippii	Spontaneous	26.27	15	229	43

Table 4: The detail of the spontaneous speech dataset (Field). The Audiobook data and the Dictionary data are not shown in this table.

Automatic Speech Recognition

Automatic speech recognition converts spoken language into text across speakers, languages, domains, and recording conditions.

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Audio transcription is a critical bottleneck in language documentation. While multilingual Automatic Speech Recognition (ASR) models like Whisper offer solutions, field linguists often lack the expertise to utilise them. We present Easper, an open-source, no-code workflow enabling linguists to iteratively fine-tune ASR models via cloud resources directly from ELAN annotations. Deploying ASR also raises a cold start problem: deciding which recordings to transcribe first to bootstrap an accurate model. Using Easper, we evaluate transcription prioritisation strategies on three Vanuatu languages (Bislama, Nafsan, Nguna). We fine-tune models by recording session, comparing Character Error Rate trajectories when prioritising acoustic cleanliness versus linguistic richness. We demonstrate that prioritising lexically rich narratives and increasing acoustic-phonetic repetition, even in noisy environments, leads to faster improvements in transcription quality.

Abstract

Automatic Speech Recognition

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