Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for Ainu Language

• URL: http://arxiv.org/abs/2002.06675v3
• Date: Sat, 16 May 2020 12:53:04 GMT
• Title: Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for Ainu Language
• Authors: Kohei Matsuura, Sei Ueno, Masato Mimura, Shinsuke Sakai, Tatsuya Kawahara
• Abstract summary: Ainu is an unwritten language that has been spoken by Ainu people who are one of the ethnic groups in Japan. It is recognized as critically endangered by UNESCO and archiving and documentation of its language heritage is of paramount importance. We started a project of automatic speech recognition (ASR) for the Ainu language in order to contribute to the development of annotated language archives.
• Score: 32.6535407800833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ainu is an unwritten language that has been spoken by Ainu people who are one of the ethnic groups in Japan. It is recognized as critically endangered by UNESCO and archiving and documentation of its language heritage is of paramount importance. Although a considerable amount of voice recordings of Ainu folklore has been produced and accumulated to save their culture, only a quite limited parts of them are transcribed so far. Thus, we started a project of automatic speech recognition (ASR) for the Ainu language in order to contribute to the development of annotated language archives. In this paper, we report speech corpus development and the structure and performance of end-to-end ASR for Ainu. We investigated four modeling units (phone, syllable, word piece, and word) and found that the syllable-based model performed best in terms of both word and phone recognition accuracy, which were about 60% and over 85% respectively in speaker-open condition. Furthermore, word and phone accuracy of 80% and 90% has been achieved in a speaker-closed setting. We also found out that a multilingual ASR training with additional speech corpora of English and Japanese further improves the speaker-open test accuracy.

Related papers

• Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages [76.14451035425229]
  We introduce Omnilingual ASR, a large-scale automatic speech recognition system.<n>It scales self-supervised pre-training to 7B parameters to learn robust speech representations.<n>It expands coverage to over 1,600 languages, including over 500 never before served by ASR.
  arXiv  Detail & Related papers  (2025-11-12T19:48:09Z)
• Towards Inclusive Communication: A Unified Framework for Generating Spoken Language from Sign, Lip, and Audio [52.859261069569165]
  We propose the first unified framework capable of handling diverse combinations of sign language, lip movements, and audio for spoken-language text generation.<n>We focus on three main objectives: (i) designing a unified, modality-agnostic architecture capable of effectively processing heterogeneous inputs; (ii) exploring the underexamined synergy among modalities, particularly the role of lip movements as non-manual cues in sign language comprehension; and (iii) achieving performance on par with or better than state-of-the-art models specialized for individual tasks.
  arXiv  Detail & Related papers  (2025-08-28T06:51:42Z)
• Zero-AVSR: Zero-Shot Audio-Visual Speech Recognition with LLMs by Learning Language-Agnostic Speech Representations [65.59784436914548]
  We introduce the Audio-Visual Speech Romanizer (AV-Romanizer), which learns language-agnostic speech representations by predicting Roman text. We convert the predicted Roman text into language-specific graphemes, forming the proposed Cascaded Zero-AVSR. To capture the wide spectrum of phonetic and linguistic diversity, we also introduce a Multilingual Audio-Visual Romanized Corpus (MARC)
  arXiv  Detail & Related papers  (2025-03-08T16:40:13Z)
• AudioPaLM: A Large Language Model That Can Speak and Listen [79.44757696533709]
  We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models. It can process and generate text and speech with applications including speech recognition and speech-to-speech translation.
  arXiv  Detail & Related papers  (2023-06-22T14:37:54Z)
• Phonemic Representation and Transcription for Speech to Text Applications for Under-resourced Indigenous African Languages: The Case of Kiswahili [0.0]
  It has emerged that several African indigenous languages, including Kiswahili, are technologically under-resourced. This paper explores the transcription process and the development of a Kiswahili speech corpus. It provides an updated Kiswahili phoneme dictionary for the ASR model that was created using the CMU Sphinx speech recognition toolbox.
  arXiv  Detail & Related papers  (2022-10-29T09:04:09Z)
• An Automatic Speech Recognition System for Bengali Language based on Wav2Vec2 and Transfer Learning [0.0]
  This paper aims to improve the speech recognition performance of the Bengali language by adopting speech recognition technology on the E2E structure based on the transfer learning framework. The proposed method effectively models the Bengali language and achieves 3.819 score in Levenshtein Mean Distance' on the test dataset of 7747 samples, when only 1000 samples of train dataset were used to train.
  arXiv  Detail & Related papers  (2022-09-16T18:20:16Z)
• ASR2K: Speech Recognition for Around 2000 Languages without Audio [100.41158814934802]
  We present a speech recognition pipeline that does not require any audio for the target language. Our pipeline consists of three components: acoustic, pronunciation, and language models. We build speech recognition for 1909 languages by combining it with Crubadan: a large endangered languages n-gram database.
  arXiv  Detail & Related papers  (2022-09-06T22:48:29Z)
• Aggression in Hindi and English Speech: Acoustic Correlates and Automatic Identification [0.802904964931021]
  The study is based on a corpus of slightly over 10 hours of political discourse. We develop two automatic classification systems for identifying aggression in English and Hindi speech.
  arXiv  Detail & Related papers  (2022-04-06T13:29:25Z)
• A study on native American English speech recognition by Indian listeners with varying word familiarity level [62.14295630922855]
  We have three kinds of responses from each listener while they recognize an utterance. From these transcriptions, word error rate (WER) is calculated and used as a metric to evaluate the similarity between the recognized and the original sentences. Speaker nativity wise analysis shows that utterances from speakers of some nativity are more difficult to recognize by Indian listeners compared to few other nativities.
  arXiv  Detail & Related papers  (2021-12-08T07:43:38Z)
• Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties [77.2347265289855]
  We focus on phoneme recognition using Allosaurus, a method for multilingual recognition based on phonetic annotation. To evaluate in a challenging real-world scenario, we curate phone recognition datasets for Bukusu and Saamia, two varieties of the Luhya language cluster of western Kenya and eastern Uganda. We find that fine-tuning of Allosaurus, even with just 100 utterances, leads to significant improvements in phone error rates.
  arXiv  Detail & Related papers  (2021-04-04T15:07:55Z)
• OkwuGb\'e: End-to-End Speech Recognition for Fon and Igbo [0.015863809575305417]
  We present a state-of-art ASR model for Fon, as well as benchmark ASR model results for Igbo. We conduct a comprehensive linguistic analysis of each language and describe the creation of end-to-end, deep neural network-based speech recognition models for both languages.
  arXiv  Detail & Related papers  (2021-03-13T18:02:44Z)
• Unsupervised Cross-lingual Representation Learning for Speech Recognition [63.85924123692923]
  XLSR learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations. Experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining.
  arXiv  Detail & Related papers  (2020-06-24T18:25:05Z)
• Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech Recognition [31.808145263757105]
  We use CycleGAN-based non-parallel voice conversion technology to forge a labeled training data that is close to the test speaker's speech. We evaluate this speaker adaptation approach on two low-resource corpora, namely, Ainu and Mboshi.
  arXiv  Detail & Related papers  (2020-05-19T07:35:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.