Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Training (2024.eacl-long)
Copied to clipboard
| Challenge: | End-to-end (E2E) spoken language understanding models are constrained by the cost of collecting speech-semantics pairs. |
| Approach: | They propose a model that learns E2E SLU without speech-semantics pairs . they propose cross-modal selective self-training (CMSST) to address imbalance and noise issues . |
| Outcome: | The proposed model learns E2E SLU without speech-semantics pairs . the proposed model requires the domains of speech-text and text-sensitization to match . |
Similar Papers
Towards Zero-shot Learning for End-to-end Cross-modal Translation Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | End-to-end zero-shot speech translation model is based on a zero-shot approach, but it is less competitive because of the limited amount of data available for multiple modalities. |
| Approach: | They propose an end-to-end zero-shot speech translation model that connects two pre-trained uni-modality modules via word rotator’s distance. |
| Outcome: | The proposed model performs better than or as well as those of the CTC-based models and can be trained in an end-to-end style to avoid error propagation. |
Zero-Shot Spoken Language Understanding via Large Language Models: A Preliminary Study (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have shown promising results in zero-shot settings, which motivates us to explore prompt-based methods. |
| Approach: | They propose a two-stage framework which transforms the SLU task into a question-answering problem by directly prompting LLMs. |
| Outcome: | The proposed framework can be built by directly prompting LLMs to understand user needs without training data. |
Pushing the Limits of Zero-shot End-to-End Speech Translation (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to end-to-end Speech Translation (ST) systems require limited data, which can cause data scarcity and performance degradation. |
| Approach: | They propose a method for zero-shot ST that bridges the modality gap without any paired ST data. |
| Outcome: | The proposed method bridges the modality gap without any paired ST data on a speech encoder and on MT models. |
FalAI: A Dataset for End-to-end Spoken Language Understanding in a Low-Resource Scenario (2024.lrec-main)
Copied to clipboard
Andres Pineiro-Martin, Carmen Garcia-Mateo, Laura Docio-Fernandez, Maria del Carmen Lopez-Perez, Jose Gandarela-Rodriguez
| Challenge: | End-to-end (E2E) Spoken Language Understanding systems extract structured information from speech signals using a single model. |
| Approach: | They propose to use a dataset to extract structured information from speech signals . they define splits for noisy audio, hesitant audio and audio where sentence has changed . |
| Outcome: | The proposed model exploits acoustic information and avoids cascading errors . falAI dataset is the largest public SLU dataset in Galician and first to be obtained in low-resource scenario. |
The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Modern artificial intelligence is characterized by large pretrained language models with strong language capabilities to be adapted to various downstream tasks. |
| Approach: | They propose to use the task of speech translation (ST) to pretrain speech models for end-to-end SLU on intra- and cross-lingual scenarios. |
| Outcome: | The proposed model achieves higher performance over baselines on monolingual and multilingual intent classification as well as spoken question answering using SLURP, MINDS-14, and NMSQA benchmarks. |
Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing zero-shot methods fail to align speech and text into a shared semantic space . Existing methods require expensive and expensive parallel ST data . |
| Approach: | They propose a method that uses a shared discrete vocabulary space to align speech and text into a common space. |
| Outcome: | The proposed method significantly improves the SOTA and even performs on par with the strong supervised ST baselines. |
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective (2022.emnlp-main)
Copied to clipboard
Ping Yang, Junjie Wang, Ruyi Gan, Xinyu Zhu, Lin Zhang, Ziwei Wu, Xinyu Gao, Jiaxing Zhang, Tetsuya Sakai
| Challenge: | Existing approaches to zero-shot learning are format-agnostic and can address new learning tasks without additional training. |
| Approach: | They propose a new paradigm for zero-shot learning that is format agnostic and compatible with any format and applicable to a list of language tasks. |
| Outcome: | The proposed model shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as text classification and commonsense reasoning. |
Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models (2022.findings-emnlp)
Copied to clipboard
| Challenge: | End-to-end spoken language understanding systems model sequence labeling as a sequence prediction task causing a divergence from its well-established token-level tagging formulation. |
| Approach: | They propose to model sequence labeling as a sequence prediction task . their systems explicitly separate the added complexity of recognizing spoken mentions from the NLU task of sequence labelling . |
| Outcome: | The proposed systems outperform both cascaded and direct models on a labeling task of named entity recognition across SLU benchmarks. |
Advancing Zero-shot Text-to-Speech Intelligibility across Diverse Domains via Preference Alignment (2025.acl-long)
Copied to clipboard
| Challenge: | Existing zero-shot text-to-speech systems struggle in challenging scenarios such as tongue twisters, repeated words, code-switching, and cross-lingual synthesis. |
| Approach: | They propose a dataset that leverages preference alignment techniques to improve performance . they also extend the Direct Preference Optimization framework to accommodate diverse TTS architectures . |
| Outcome: | The proposed dataset improves intelligibility, similarity, and audio quality for multiple models across domains. |
Low-Resource Multilingual and Zero-Shot Multispeaker TTS (2022.aacl-main)
Copied to clipboard
| Challenge: | Currently, the amount of data needed for TTS is limited to the vast majority of the spoken languages. |
| Approach: | They propose to use language agnostic meta learning procedure to learn speaking a new language with just 5 minutes of training data while retaining the ability to infer the voice of even unseen speakers. |
| Outcome: | The proposed approach is able to learn speaking a new language using just 5 minutes of training data while retaining the ability to infer the voice of even unseen speakers in the newly learned language. |