SBAAM! Eliminating Transcript Dependency in Automatic Subtitling (2024.acl-long)
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| Challenge: | Subtitling is a crucial task for enhancing the accessibility of audiovisual content and relying on automatic transcripts for the three subtasks is uncharted territory. |
| Approach: | They propose a model capable of producing automatic subtitles, completely eliminating any dependence on intermediate transcripts also for timestamp prediction. |
| Outcome: | Experimental results show that the proposed model eliminates the need for intermediate transcripts for timestamp prediction across multiple language pairs and diverse conditions. |
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| Challenge: | Existing models for automatic subtitling generate subtitles in the target language along with their timestamps. |
| Approach: | They propose a direct speech translation model that generates subtitles in the target language along with their timestamps with a single model. |
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Dodging the Data Bottleneck: Automatic Subtitling with Automatically Segmented ST Corpora (2022.aacl-short)
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| Challenge: | Existing models for subtitling require parallel data paired with audio inputs and textual translations. |
| Approach: | They propose to convert existing ST corpora into SubST resources without human intervention by exploiting audio and text in a multimodal fashion. |
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MuST-Cinema: a Speech-to-Subtitles corpus (2020.lrec-1)
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| Challenge: | Existing methods for subtitling are laborious and costly, says aaron sanchez . he says the current methods are laboriously complex and require manual work . |
| Approach: | They propose to use TED subtitles to build a multilingual speech translation corpus . they propose to annotate existing subtitling corpora with subtitle breaks . |
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Dubbing in Practice: A Large Scale Study of Human Localization With Insights for Automatic Dubbing (2023.tacl-1)
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| Challenge: | a large-scale study of human dubbing in practice is lacking in qualitative literature on human dubs . authors argue for vocal naturalness and translation quality over isometric constraints . a data-driven examination of the way humans perform this task is needed . |
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AudioCaps: Generating Captions for Audios in The Wild (N19-1)
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| Challenge: | a dataset of 46K audio clips with human-written text pairs is used to generate captions for audio . the task of translating a multimedia input source into natural language has been extensively studied over the past few years . |
| Approach: | They propose a top-down multi-scale encoder and aligned semantic attention for audio captioning. |
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Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
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Consistent Transcription and Translation of Speech (2020.tacl-1)
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| Challenge: | Existing models that translate without transcribing focus on translation quality, while transcription receives less emphasis. |
| Approach: | They propose a method to evaluate consistency and compare different approaches . they propose 'coupled inference' models that feature a coupled inference procedure can achieve strong consistency. |
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The importance of fillers for text representations of speech transcripts (2020.emnlp-main)
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| Challenge: | Fillers are a type of disfluency that can be a sound ("um" or "uh") filling a pause in an utterance or conversation. |
| Approach: | They propose to represent fillers with deep contextualised embeddings to improve modelling of spoken language and two downstream tasks . |
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SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations (2023.findings-emnlp)
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| Challenge: | End-to-end Speech Translation models are limited by a data bottleneck . end-to end models can address several shortcomings of cascaded models . |
| Approach: | They propose a data augmentation strategy to augment sentence-level datasets by using an Audio Segmentation system to re-segment the speech of each document with different length constraints. |
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Segmenting Subtitles for Correcting ASR Segmentation Errors (2021.eacl-main)
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David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galuscakova, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown
| Challenge: | Typical ASR systems segment input audio into utterances using purely acoustic information, which may not resemble sentence-like units expected by conventional machine translation systems for spoken language translation (SLT). |
| Approach: | They propose a model for correcting ASR acoustic segmentation using subtitles as a proxy dataset for creating synthetic aural utterances by modeling common error modes. |
| Outcome: | The proposed model improves performance on MT and audio-document cross-language information retrieval (CLIR) it uses subtitles as a proxy dataset to correct ASR acoustic segmentation . |