Papers with cascaded models
Streaming Models for Joint Speech Recognition and Translation (2021.eacl-main)
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| Challenge: | Using end-to-end models for speech translation has become a focus of the ST community . cascaded models have the advantage of including automatic speech recognition output . |
| Approach: | They propose a model that condenses sound waves into translated text and integrates automatic speech recognition outputs into the models. |
| Outcome: | The proposed model is statistically similar to cascading models, but has half the number of parameters. |
RealTranS: End-to-End Simultaneous Speech Translation with Convolutional Weighted-Shrinking Transformer (2021.findings-acl)
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| Challenge: | End-to-end simultaneous speech translation (SST) is useful in many scenarios but has not been fully investigated. |
| Approach: | They propose an end-to-end simultaneous speech translation model called RealTranS . they use interleaved convolution and unidirectional Transformer layers to downsample input speech . |
| Outcome: | The proposed model outperforms existing models on public and widely-used datasets in multiple latency settings. |
Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation (2024.findings-acl)
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| Challenge: | In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image with translations in target language. |
| Approach: | They propose an end-to-end IIMT model with four modules that translate images . they propose a two-stage training framework to assist the model in learning alignment across languages . |
| Outcome: | The proposed model outperforms cascaded models with only 70.9% of parameters and is highly accurate. |
Can We Achieve High-quality Direct Speech-to-Speech Translation without Parallel Speech Data? (2024.acl-long)
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| Challenge: | Existing two-pass direct speech-to-speech translation models require parallel speech data to train, which is challenging to collect. |
| Approach: | They propose a two-pass direct speech-to-speech translation (S2ST) model that decomposes the task into speech- to-text translation (s2TT) and text-tospech (TTS) they propose 'composer' S2ST model that integrates pretrained S2TT and TTS models into a direct S2 ST model. |
| Outcome: | The proposed model integrates pretrained S2TT and TTS models into a direct S2ST model without parallel speech data. |
CCSRD: Content-Centric Speech Representation Disentanglement Learning for End-to-End Speech Translation (2023.findings-emnlp)
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| Challenge: | Existing speech-to-text translation models can extract features from speech inputs, but they may include non-linguistic speech factors such as pitch, timbre and speaker identity. |
| Approach: | They propose a content-centric speech representation disentanglement learning framework for speech translation that decomposes speech representations into content representations and non-linguistic representations via representation disentanglement learning. |
| Outcome: | The proposed framework outperforms state-of-the-art speech translation models and cascaded models on five translation directions. |
Misogyny and Aggressiveness Tend to Come Together and Together We Address Them (2022.lrec-1)
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| Challenge: | Using a binary task to identify whether a tweet is misogynous and aggressive, we compare two approaches to address these problems: one multi-class model that discriminates between all the classes at once; and a cascaded approach where the binary classification is carried out separately. |
| Approach: | They propose a multi-class model that discriminates between all the classes at once and a cascaded approach where the binary classification is carried out separately and then joined together. |
| Outcome: | The proposed models outperform the top submissions to Evalita on the 2020 shared task on automatic misogyny and aggressiveness identification in Italian tweets. |
Speech Translation and the End-to-End Promise: Taking Stock of Where We Are (2020.acl-main)
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| Challenge: | Until recently, the only feasible approach to translating acoustic speech signals into text was the cascaded approach. |
| Approach: | They propose a classification of the main challenges of traditional approaches to speech translation . they argue that end-to-end models fall short due to compromises made to address data scarcity . |
| Outcome: | This paper provides a brief survey of the main challenges of traditional approaches in speech translation . it reveals that many end-to-end models fail due to compromises made to address data scarcity. |