Developing multilingual speech synthesis system for Ojibwe, Mi'kmaq, and Maliseet

• URL: http://arxiv.org/abs/2502.02703v1
• Date: Tue, 04 Feb 2025 20:36:55 GMT
• Title: Developing multilingual speech synthesis system for Ojibwe, Mi'kmaq, and Maliseet
• Authors: Shenran Wang, Changbing Yang, Mike Parkhill, Chad Quinn, Christopher Hammerly, Jian Zhu,
• Abstract summary: We present lightweight flow matching multilingual text-to-speech (TTS) systems for Ojibwe, Mi'kmaq, and Maliseet, three Indigenous languages in North America. Our results show that training a multilingual TTS model on three typologically similar languages can improve the performance over monolingual models.
• Score: 4.889851090443267
• License:
• Abstract: We present lightweight flow matching multilingual text-to-speech (TTS) systems for Ojibwe, Mi'kmaq, and Maliseet, three Indigenous languages in North America. Our results show that training a multilingual TTS model on three typologically similar languages can improve the performance over monolingual models, especially when data are scarce. Attention-free architectures are highly competitive with self-attention architecture with higher memory efficiency. Our research not only advances technical development for the revitalization of low-resource languages but also highlights the cultural gap in human evaluation protocols, calling for a more community-centered approach to human evaluation.

Related papers

• Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection [49.27067541740956]
  Speech Emotion Recognition (SER) is a crucial component in developing general-purpose AI agents capable of natural human-computer interaction. Building robust multilingual SER systems remains challenging due to the scarcity of labeled data in languages other than English and Chinese. We propose an approach to enhance SER performance in low SER resource languages by leveraging data from high-resource languages.
  arXiv  Detail & Related papers  (2024-09-17T08:36:45Z)
• SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages [77.75535024869224]
  We present SeaLLMs 3, the latest iteration of the SeaLLMs model family, tailored for Southeast Asian languages. SeaLLMs 3 aims to bridge this gap by covering a comprehensive range of languages spoken in this region, including English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese. Our model excels in tasks such as world knowledge, mathematical reasoning, translation, and instruction following, achieving state-of-the-art performance among similarly sized models.
  arXiv  Detail & Related papers  (2024-07-29T03:26:22Z)
• SUTRA: Scalable Multilingual Language Model Architecture [5.771289785515227]
  We introduce SUTRA, a multilingual Large Language Model architecture capable of understanding, reasoning, and generating text in over 50 languages. Through extensive evaluations, SUTRA is demonstrated to surpass existing models like GPT-3.5, Llama2 by 20-30% on leading Massive Multitask Language Understanding (MMLU) benchmarks. Our findings suggest that SUTRA not only fills pivotal gaps in multilingual model capabilities but also establishes a new benchmark for operational efficiency and scalability in AI applications.
  arXiv  Detail & Related papers  (2024-05-07T20:11:44Z)
• Compass: Large Multilingual Language Model for South-east Asia [0.0]
  CompassLLM is a large multilingual model specifically tailored for Southeast Asian languages. Our model exhibits its superior performance in South-east Asia languages, such as Indonesian language.
  arXiv  Detail & Related papers  (2024-04-14T11:48:33Z)
• Multilingual Word Embeddings for Low-Resource Languages using Anchors and a Chain of Related Languages [54.832599498774464]
  We propose to build multilingual word embeddings (MWEs) via a novel language chain-based approach. We build MWEs one language at a time by starting from the resource rich source and sequentially adding each language in the chain till we reach the target. We evaluate our method on bilingual lexicon induction for 4 language families, involving 4 very low-resource (5M tokens) and 4 moderately low-resource (50M) target languages.
  arXiv  Detail & Related papers  (2023-11-21T09:59:29Z)
• Learning to translate by learning to communicate [11.43638897327485]
  We formulate and test a technique to use Emergent Communication (EC) with a pre-trained multilingual model to improve on modern Unsupervised NMT systems. In our approach, we embed a multilingual model into an EC image-reference game, in which the model is incentivized to use multilingual generations to accomplish a vision-grounded task. We present two variants of EC Fine-Tuning (Steinert-Threlkeld et al., 2022), one of which outperforms a backtranslation-only baseline in all four languages investigated.
  arXiv  Detail & Related papers  (2022-07-14T15:58:06Z)
• No Language Left Behind: Scaling Human-Centered Machine Translation [69.28110770760506]
  We create datasets and models aimed at narrowing the performance gap between low and high-resource languages. We propose multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art.
  arXiv  Detail & Related papers  (2022-07-11T07:33:36Z)
• Generalizing Multimodal Pre-training into Multilingual via Language Acquisition [54.69707237195554]
  English-based Vision-Language Pre-training has achieved great success in various downstream tasks. Some efforts have been taken to generalize this success to non-English languages through Multilingual Vision-Language Pre-training. We propose a textbfMultitextbfLingual textbfAcquisition (MLA) framework that can easily generalize a monolingual Vision-Language Pre-training model into multilingual.
  arXiv  Detail & Related papers  (2022-05-29T08:53:22Z)
• Overcoming Language Disparity in Online Content Classification with Multimodal Learning [22.73281502531998]
  Large language models are now the standard to develop state-of-the-art solutions for text detection and classification tasks. The development of advanced computational techniques and resources is disproportionately focused on the English language. We explore the promise of incorporating the information contained in images via multimodal machine learning.
  arXiv  Detail & Related papers  (2022-05-19T17:56:02Z)
• Improving Multilingual Neural Machine Translation For Low-Resource Languages: French-, English- Vietnamese [4.103253352106816]
  This paper proposes two simple strategies to address the rare word issue in multilingual MT systems for two low-resource language pairs: French-Vietnamese and English-Vietnamese. We have shown significant improvements of up to +1.62 and +2.54 BLEU points over the bilingual baseline systems for both language pairs.
  arXiv  Detail & Related papers  (2020-12-16T04:43:43Z)
• Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation [105.41167108465085]
  Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages. We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC) LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language. We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
  arXiv  Detail & Related papers  (2020-10-27T13:12:17Z)

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.