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Found 10 papers, 2 papers with code
Seamless: Multilingual Expressive and Streaming Speech Translation
In this work, we introduce a family of models that enable end-to-end expressive and multilingual translations in a streaming fashion.
Exploring Speech Enhancement for Low-resource Speech Synthesis
High-quality and intelligible speech is essential to text-to-speech (TTS) model training, however, obtaining high-quality data for low-resource languages is challenging and expensive.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders
Large-scale self-supervised pre-trained speech encoders outperform conventional approaches in speech recognition and translation tasks.
SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages?
Ranked #1 on
Machine Translation
on flores95-devtest eng-X
Automatic Speech Recognition
Speech-to-Speech Translation
+3
Multilingual Speech-to-Speech Translation into Multiple Target Languages
Despite a few studies on multilingual S2ST, their focus is the multilinguality on the source side, i. e., the translation from multiple source languages to one target language.
Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks
In order to leverage strengths of both modeling methods, we propose a solution by combining Transducer and Attention based Encoder-Decoder (TAED) for speech-to-text tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations
We present SpeechMatrix, a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings.
Unified Speech-Text Pre-training for Speech Translation and Recognition
Two pre-training configurations for speech translation and recognition, respectively, are presented to alleviate subtask interference.
Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation
This paper proposes a simple and effective approach to address the problem of posterior collapse in conditional variational autoencoders (CVAEs).
Improved Variational Neural Machine Translation by Promoting Mutual Information
Posterior collapse plagues VAEs for text, especially for conditional text generation with strong autoregressive decoders.
