Efficient neural speech synthesis for low-resource languages through multilingual modeling

• URL: http://arxiv.org/abs/2008.09659v1
• Date: Thu, 20 Aug 2020 14:05:28 GMT
• Title: Efficient neural speech synthesis for low-resource languages through multilingual modeling
• Authors: Marcel de Korte, Jaebok Kim, Esther Klabbers
• Abstract summary: Multi-speaker modeling can reduce the data requirements necessary for a new voice. We show that multilingual models can produce speech with a naturalness comparable to monolingual multi-speaker models.
• Score: 3.996275177789896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in neural TTS have led to models that can produce high-quality synthetic speech. However, these models typically require large amounts of training data, which can make it costly to produce a new voice with the desired quality. Although multi-speaker modeling can reduce the data requirements necessary for a new voice, this approach is usually not viable for many low-resource languages for which abundant multi-speaker data is not available. In this paper, we therefore investigated to what extent multilingual multi-speaker modeling can be an alternative to monolingual multi-speaker modeling, and explored how data from foreign languages may best be combined with low-resource language data. We found that multilingual modeling can increase the naturalness of low-resource language speech, showed that multilingual models can produce speech with a naturalness comparable to monolingual multi-speaker models, and saw that the target language naturalness was affected by the strategy used to add foreign language data.

Related papers

• A multilingual training strategy for low resource Text to Speech [5.109810774427171]
  We investigate whether data from social media can be used for a small TTS dataset construction, and whether cross lingual transfer learning can work with this type of data. To do so, we explore how data from foreign languages may be selected and pooled to train a TTS model for a target low resource language. Our findings show that multilingual pre-training is better than monolingual pre-training at increasing the intelligibility and naturalness of the generated speech.
  arXiv  Detail & Related papers  (2024-09-02T12:53:01Z)
• ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information Retrieval [10.664434993386523]
  Current approaches circumvent the lack of high-quality labeled data in non-English languages. We present a novel modular dense retrieval model that learns from the rich data of a single high-resource language.
  arXiv  Detail & Related papers  (2024-02-23T02:21:24Z)
• Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon [78.12363425794214]
  We focus on zero-shot sentiment analysis tasks across 34 languages, including 6 high/medium-resource languages, 25 low-resource languages, and 3 code-switching datasets. We demonstrate that pretraining using multilingual lexicons, without using any sentence-level sentiment data, achieves superior zero-shot performance compared to models fine-tuned on English sentiment datasets.
  arXiv  Detail & Related papers  (2024-02-03T10:41:05Z)
• Adapting Multilingual Speech Representation Model for a New, Underresourced Language through Multilingual Fine-tuning and Continued Pretraining [2.3513645401551333]
  We investigate the possibility for adapting an existing multilingual wav2vec 2.0 model for a new language. Our results show that continued pretraining is the most effective method to adapt a wav2vec 2.0 model for a new language. We find that if a model pretrained on a related speech variety or an unrelated language with similar phonological characteristics is available, multilingual fine-tuning using additional data from that language can have positive impact on speech recognition performance.
  arXiv  Detail & Related papers  (2023-01-18T03:57:53Z)
• LAMASSU: Streaming Language-Agnostic Multilingual Speech Recognition and Translation Using Neural Transducers [71.76680102779765]
  Automatic speech recognition (ASR) and speech translation (ST) can both use neural transducers as the model structure. We propose LAMASSU, a streaming language-agnostic multilingual speech recognition and translation model using neural transducers.
  arXiv  Detail & Related papers  (2022-11-05T04:03:55Z)
• A Survey of Multilingual Models for Automatic Speech Recognition [6.657361001202456]
  Cross-lingual transfer is an attractive solution to the problem of multilingual Automatic Speech Recognition. Recent advances in Self Supervised Learning are opening up avenues for unlabeled speech data to be used in multilingual ASR models. We present best practices for building multilingual models from research across diverse languages and techniques.
  arXiv  Detail & Related papers  (2022-02-25T09:31:40Z)
• Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity [81.51206991542242]
  Cross-lingual transfer offers a compelling way to help bridge this digital divide. Current cross-lingual algorithms have shown success in text-based tasks and speech-related tasks over some low-resource languages. We propose a language similarity approach that can efficiently identify acoustic cross-lingual transfer pairs across hundreds of languages.
  arXiv  Detail & Related papers  (2021-11-02T01:55:17Z)
• xGQA: Cross-Lingual Visual Question Answering [100.35229218735938]
  xGQA is a new multilingual evaluation benchmark for the visual question answering task. We extend the established English GQA dataset to 7 typologically diverse languages. We propose new adapter-based approaches to adapt multimodal transformer-based models to become multilingual.
  arXiv  Detail & Related papers  (2021-09-13T15:58:21Z)
• Discovering Representation Sprachbund For Multilingual Pre-Training [139.05668687865688]
  We generate language representation from multilingual pre-trained models and conduct linguistic analysis. We cluster all the target languages into multiple groups and name each group as a representation sprachbund. Experiments are conducted on cross-lingual benchmarks and significant improvements are achieved compared to strong baselines.
  arXiv  Detail & Related papers  (2021-09-01T09:32:06Z)
• Specializing Multilingual Language Models: An Empirical Study [50.7526245872855]
  Contextualized word representations from pretrained multilingual language models have become the de facto standard for addressing natural language tasks. For languages rarely or never seen by these models, directly using such models often results in suboptimal representation or use of data.
  arXiv  Detail & Related papers  (2021-06-16T18:13:55Z)
• Evaluating Cross-Lingual Transfer Learning Approaches in Multilingual Conversational Agent Models [1.52292571922932]
  We propose a general multilingual model framework for Natural Language Understanding (NLU) models. We show that these multilingual models can reach same or better performance compared to monolingual models across language-specific test data.
  arXiv  Detail & Related papers  (2020-12-07T17:14:52Z)

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.