1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis
- URL: http://arxiv.org/abs/2406.11727v2
- Date: Thu, 27 Jun 2024 08:52:54 GMT
- Title: 1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis
- Authors: Sewade Ogun, Abraham T. Owodunni, Tobi Olatunji, Eniola Alese, Babatunde Oladimeji, Tejumade Afonja, Kayode Olaleye, Naome A. Etori, Tosin Adewumi,
- Abstract summary: Afro-TTS is the first pan-African English accented speech synthesis system.
Speaker retains naturalness and accentedness, enabling the creation of new voices.
- Score: 1.7606944034136094
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in speech synthesis have enabled many useful applications like audio directions in Google Maps, screen readers, and automated content generation on platforms like TikTok. However, these systems are mostly dominated by voices sourced from data-rich geographies with personas representative of their source data. Although 3000 of the world's languages are domiciled in Africa, African voices and personas are under-represented in these systems. As speech synthesis becomes increasingly democratized, it is desirable to increase the representation of African English accents. We present Afro-TTS, the first pan-African accented English speech synthesis system able to generate speech in 86 African accents, with 1000 personas representing the rich phonological diversity across the continent for downstream application in Education, Public Health, and Automated Content Creation. Speaker interpolation retains naturalness and accentedness, enabling the creation of new voices.
Related papers
- Accent conversion using discrete units with parallel data synthesized from controllable accented TTS [56.18382038512251]
The goal of accent conversion (AC) is to convert speech accents while preserving content and speaker identity.
Previous methods either required reference utterances during inference, did not preserve speaker identity well, or used one-to-one systems that could only be trained for each non-native accent.
This paper presents a promising AC model that can convert many accents into native to overcome these issues.
arXiv Detail & Related papers (2024-09-30T19:52:10Z) - 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) - Accent Conversion in Text-To-Speech Using Multi-Level VAE and Adversarial Training [14.323313455208183]
Inclusive speech technology aims to erase any biases towards specific groups, such as people of certain accent.
We propose a TTS model that utilizes a Multi-Level Variational Autoencoder with adversarial learning to address accented speech synthesis and conversion.
arXiv Detail & Related papers (2024-06-03T05:56:02Z) - 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) - NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot
Speech and Singing Synthesizers [90.83782600932567]
We develop NaturalSpeech 2, a TTS system that leverages a neural audio predictor with residual vectorizers to get the quantized latent vectors.
We scale NaturalSpeech 2 to large-scale datasets with 44K hours of speech and singing data and evaluate its voice quality on unseen speakers.
NaturalSpeech 2 outperforms previous TTS systems by a large margin in terms of prosody/timbre similarity, synthesis, and voice quality in a zero-shot setting.
arXiv Detail & Related papers (2023-04-18T16:31:59Z) - AfroDigits: A Community-Driven Spoken Digit Dataset for African
Languages [32.23306825605942]
AfroDigits is a minimalist dataset of spoken digits for African languages.
We conduct audio digit classification experiments on six African languages.
AfroDigits is the first published audio digit dataset for African languages.
arXiv Detail & Related papers (2023-03-22T14:09:20Z) - Building African Voices [125.92214914982753]
This paper focuses on speech synthesis for low-resourced African languages.
We create a set of general-purpose instructions on building speech synthesis systems with minimum technological resources.
We release the speech data, code, and trained voices for 12 African languages to support researchers and developers.
arXiv Detail & Related papers (2022-07-01T23:28:16Z) - Deep Speech Based End-to-End Automated Speech Recognition (ASR) for
Indian-English Accents [0.0]
We have used transfer learning approach to develop an end-to-end speech recognition system for Indian-English accents.
Indic TTS data of Indian-English accents is used for transfer learning and fine-tuning the pre-trained Deep Speech model.
arXiv Detail & Related papers (2022-04-03T03:11:21Z) - Textless Speech-to-Speech Translation on Real Data [49.134208897722246]
We present a textless speech-to-speech translation (S2ST) system that can translate speech from one language into another language.
We tackle the challenge in modeling multi-speaker target speech and train the systems with real-world S2ST data.
arXiv Detail & Related papers (2021-12-15T18:56:35Z) - Using Radio Archives for Low-Resource Speech Recognition: Towards an
Intelligent Virtual Assistant for Illiterate Users [3.3946853660795884]
In many countries, illiterate people tend to speak only low-resource languages.
We investigate the effectiveness of unsupervised speech representation learning on noisy radio broadcasting archives.
Our contributions offer a path forward for ethical AI research to serve the needs of those most disadvantaged by the digital divide.
arXiv Detail & Related papers (2021-04-27T10:09:34Z)
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.