Finnish Dialect Identification: The Effect of Audio and Text
- URL: http://arxiv.org/abs/2111.03800v1
- Date: Sat, 6 Nov 2021 04:25:53 GMT
- Title: Finnish Dialect Identification: The Effect of Audio and Text
- Abstract summary: We present the first approach to automatically detect the dialect of a speaker based on a dialect transcript and transcript with audio recording.
Our results show that the best accuracy is received by combining both of the modalities.
- Score: 1.0896567381206714
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Finnish is a language with multiple dialects that not only differ from each
other in terms of accent (pronunciation) but also in terms of morphological
forms and lexical choice. We present the first approach to automatically detect
the dialect of a speaker based on a dialect transcript and transcript with
audio recording in a dataset consisting of 23 different dialects. Our results
show that the best accuracy is received by combining both of the modalities, as
text only reaches to an overall accuracy of 57\%, where as text and audio reach
to 85\%. Our code, models and data have been released openly on Github and
Zenodo.
Related papers
- ARCADE: A City-Scale Corpus for Fine-Grained Arabic Dialect Tagging [4.23980289430769]
We present ARCADE, the first Arabic speech dataset designed explicitly with city-level dialect granularity.<n>The corpus comprises Arabic radio speech collected from streaming services across the Arab world.<n>The resulting corpus comprises 6,907 annotations and 3,790 unique audio segments spanning 58 cities across 19 countries.
arXiv Detail & Related papers (2026-01-05T15:32:17Z) - Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects [36.91800117379075]
We compare standard-to-dialect transfer in three settings: text models, speech models, and cascaded systems.<n>In our experiments, we focus on German and multiple German dialects in the context of written and spoken intent and topic classification.<n>We find that the speech-only setup provides the best results on the dialect data while the text-only setup works best on the standard data.
arXiv Detail & Related papers (2025-10-09T07:43:08Z) - Languages in Multilingual Speech Foundation Models Align Both Phonetically and Semantically [58.019484208091534]
Cross-lingual alignment in pretrained language models (LMs) has enabled efficient transfer in text-based LMs.<n>It remains an open question whether findings and methods from text-based cross-lingual alignment apply to speech.
arXiv Detail & Related papers (2025-05-26T07:21:20Z) - The Development of a Comprehensive Spanish Dictionary for Phonetic and Lexical Tagging in Socio-phonetic Research (ESPADA) [0.0]
I present the creation of a comprehensive pronunciation dictionary in Spanish (ESPADA) that can be used in most of the dialect variants of Spanish data.
ESPADA is the most complete dictionary with more than 628,000 entries, representing words from 16 countries.
This aims to equip socio-phonetic researchers with a complete open-source tool that enhances dialectal research within socio-phonetic frameworks in the Spanish language.
arXiv Detail & Related papers (2024-07-22T04:51:33Z) - Voices Unheard: NLP Resources and Models for Yorùbá Regional Dialects [72.18753241750964]
Yorub'a is an African language with roughly 47 million speakers.
Recent efforts to develop NLP technologies for African languages have focused on their standard dialects.
We take steps towards bridging this gap by introducing a new high-quality parallel text and speech corpus.
arXiv Detail & Related papers (2024-06-27T22:38:04Z) - Towards spoken dialect identification of Irish [5.1121440213561335]
The Irish language is rich in its diversity of dialects and accents.
A recent study investigating dialect bias in Irish ASR found that performance for the Ulster dialect was consistently worse than for the Connacht or Munster dialects.
The present experiments investigate spoken dialect identification of Irish, with a view to incorporating such a system into the speech recognition pipeline.
arXiv Detail & Related papers (2023-07-14T16:03:09Z) - 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) - End-to-End Automatic Speech Recognition model for the Sudanese Dialect [0.0]
This paper comes to inspect the viability of designing an Automatic Speech Recognition model for the Sudanese dialect.
The paper gives an overview of the Sudanese dialect and the tasks of collecting represented resources and pre-processing performed to construct a modest dataset.
The designed model provided some insights into the current recognition task and reached an average Label Error Rate of 73.67%.
arXiv Detail & Related papers (2022-12-21T07:35:33Z) - A Highly Adaptive Acoustic Model for Accurate Multi-Dialect Speech
Recognition [80.87085897419982]
We propose a novel acoustic modeling technique for accurate multi-dialect speech recognition with a single AM.
Our proposed AM is dynamically adapted based on both dialect information and its internal representation, which results in a highly adaptive AM for handling multiple dialects simultaneously.
The experimental results on large scale speech datasets show that the proposed AM outperforms all the previous ones, reducing word error rates (WERs) by 8.11% relative compared to a single all-dialects AM and by 7.31% relative compared to dialect-specific AMs.
arXiv Detail & Related papers (2022-05-06T06:07:09Z) - Automatic Dialect Density Estimation for African American English [74.44807604000967]
We explore automatic prediction of dialect density of the African American English (AAE) dialect.
dialect density is defined as the percentage of words in an utterance that contain characteristics of the non-standard dialect.
We show a significant correlation between our predicted and ground truth dialect density measures for AAE speech in this database.
arXiv Detail & Related papers (2022-04-03T01:34:48Z) - English Accent Accuracy Analysis in a State-of-the-Art Automatic Speech
Recognition System [3.4888132404740797]
We evaluate a state-of-the-art automatic speech recognition model, using unseen data from a corpus with a wide variety of labeled English accents.
We show that there is indeed an accuracy bias in terms of accentual variety, favoring the accents most prevalent in the training corpus.
arXiv Detail & Related papers (2021-05-09T08:24:33Z) - 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) - Learning to Recognize Dialect Features [21.277962038423123]
We introduce the task of dialect feature detection, and present two multitask learning approaches.
We train our models on a small number of minimal pairs, building on how linguists typically define dialect features.
arXiv Detail & Related papers (2020-10-23T23:25:00Z) - Towards Zero-shot Learning for Automatic Phonemic Transcription [82.9910512414173]
A more challenging problem is to build phonemic transcribers for languages with zero training data.
Our model is able to recognize unseen phonemes in the target language without any training data.
It achieves 7.7% better phoneme error rate on average over a standard multilingual model.
arXiv Detail & Related papers (2020-02-26T20:38:42Z)
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.