Leibny Paola Garcia Perera

Also published as: Leibny Paola Garcia Perera


2024

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Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model
Xiangyu Zhang | Daijiao Liu | Hexin Liu | Qiquan Zhang | Hanyu Meng | Leibny Paola Garcia Perera | EngSiong Chng | Lina Yao
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Recently, Denoising Diffusion Probabilistic Models (DDPMs) have attained leading performances across a diverse range of generative tasks. However, in the field of speech synthesis, although DDPMs exhibit impressive performance, their prolonged training duration and substantial inference costs hinder practical deployment. Existing approaches primarily focus on enhancing inference speed, while approaches to accelerate training—a key factor in the costs associated with adding or customizing voices—often necessitate complex modifications to the model, compromising their universal applicability. To address the aforementioned challenges, we propose an inquiry: is it possible to enhance the training/inference speed and performance of DDPMs by modifying the speech signal itself? In this paper, we double the training and inference speed of Speech DDPMs by simply redirecting the generative target to the wavelet domain. This method not only achieves comparable or superior performance to the original model in speech synthesis tasks but also demonstrates its versatility. By investigating and utilizing different wavelet bases, our approach proves effective not just in speech synthesis, but also in speech enhancement.

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Where are you from? Geolocating Speech and Applications to Language Identification
Patrick Foley | Matthew Wiesner | Bismarck Odoom | Leibny Paola Garcia Perera | Kenton Murray | Philipp Koehn
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

We train models to answer the question, Where are you from? and show how such models can be repurposed for language identification (LID). To our knowledge, this paper is the first to introduce data sources, methods and models to tackle the task of geolocation of speech at a global scale, and the first to explore using geolocation as a proxy-task for LID. Specifically, we explore whether radio broadcasts with known origin can be used to train regression and classification-based models for geolocating speech. We build models on top of self-supervised pretrained models, using attention pooling to qualitatively verify that the model geolocates the speech itself, and not other channel artifacts.The best geolocation models localize speaker origin to around 650km. We confirm the value of speech geolocation as a proxy task by using speech geolocation models for zero-shot LID. Finally, we show that fine-tuning geolocation models for LID outperforms fine-tuning pretrained Wav2Vec2.0 models, and achieves state-of-the-art performance on the FLEURS benchmark.

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Speech Data from Radio Broadcasts for Low Resource Languages
Bismarck Bamfo Odoom | Leibny Paola Garcia Perera | Prangthip Hansanti | Loic Barrault | Christophe Ropers | Matthew Wiesner | Kenton Murray | Alexandre Mourachko | Philipp Koehn
Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)

We created a collection of speech data for 48 low resource languages. The corpus is extracted from radio broadcasts and processed with novel speech detection and language identification models based on a manually vetted subset of the audio for 10 languages. The data is made publicly available.