| Challenge: | Pre-trained speech models have advanced speech-related tasks, including speech recognition and translation. |
| Approach: | They propose a pre-trained speech model that incorporates modifications to ensure consistent speech representations during training and inference phases for streaming speech inputs. |
| Outcome: | The proposed model outperforms baseline models on speech recognition and translation tasks and achieves a superior balance between quality and latency. |
Similar Papers
Streaming Models for Joint Speech Recognition and Translation (2021.eacl-main)
Copied to clipboard
| 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. |
RedApt: An Adaptor for wav2vec 2 EncodingFaster and Smaller Speech Translation without Quality Compromise (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained speech Transformers in speech translation systems have facilitated state-of-the-art (SotA) results, but their computational cost is high. |
| Approach: | They propose a Reducer Adaptor block that could be seamlessly integrated within any Transformer-based speech encoding architecture. |
| Outcome: | The proposed Reducer Adaptor block outperforms the existing SotA architecture by an average of 0.68 BLEU score on 8 language pairs from Must-C. |
Speech Translation and the End-to-End Promise: Taking Stock of Where We Are (2020.acl-main)
Copied to clipboard
| 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. |
SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale (2022.emnlp-industry)
Copied to clipboard
Raphael Tang, Karun Kumar, Gefei Yang, Akshat Pandey, Yajie Mao, Vladislav Belyaev, Madhuri Emmadi, Craig Murray, Ferhan Ture, Jimmy Lin
| Challenge: | End-to-end automatic speech recognition systems require thousands of hours of manual annotation and heavyweight computation to perform inference. |
| Approach: | They propose to use a third-party ASR system as a weak supervision source and labeling functions derived from implicit user feedback to reduce human labor. |
| Outcome: | The proposed system improves word-error rate and speed up 600% over third-party ASR. |
Wav2Prompt: End-to-End Speech Prompt Learning and Task-based Fine-tuning for Text-based LLMs (2025.naacl-long)
Copied to clipboard
| Challenge: | Text-based large language models (LLMs) can be applied to a wide range of tasks without being explicitly trained. |
| Approach: | They propose a method which integrates spoken input with a text-based large language model (LLM) it takes LLM token embeddings as training targets and utilises a continuous integrate-and-fire mechanism for explicit speech-text alignment. |
| Outcome: | The proposed model can be applied to speech translation, speech understanding and spoken-query-based question answering tasks. |
Self-supervised Rewiring of Pre-trained Speech Encoders:
Towards Faster Fine-tuning with Less Labels in Speech Processing (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained speech encoders have facilitated great success across various speech processing tasks, but fine-tuning them for downstream tasks requires large training data to converge or to achieve state-of-the-art. |
| Approach: | They propose to rewire pre-trained speech encoders to improve their representation space without task-specific labels by neutrally synthesising audio inputs and frame masking. |
| Outcome: | The proposed model shows consistent improvement in isotropy in the representation space on 6 speech processing tasks. |
Massive End-to-end Speech Recognition Models with Time Reduction (2024.naacl-long)
Copied to clipboard
Weiran Wang, Rohit Prabhavalkar, Haozhe Shan, Zhong Meng, Dongseong Hwang, Qiujia Li, Khe Chai Sim, Bo Li, James Qin, Xingyu Cai, Adam Stooke, Chengjian Zheng, Yanzhang He, Tara Sainath, Pedro Moreno Mengibar
| Challenge: | Using the neural architecture of Google’s universal speech model, we reduce the frame rate and speed up training and inference. |
| Approach: | They propose to use the neural architecture of Google’s universal speech model with additional funnel pooling layers to significantly reduce the frame rate and speed up training and inference. |
| Outcome: | The proposed methods work with both connectionist temporal classification (CTC) and RNN-Transducer (RNN-T) and over two domains. |
Wav2SQL: Direct Generalizable Speech-To-SQL Parsing (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing models for speech-driven SQL parsing are based on a cascaded approach, resulting in data scarcity and inconsistent performance. |
| Approach: | They propose a direct generalizable speech-to-SQL parsing model which avoids error compounding across cascaded systems. |
| Outcome: | The proposed model avoids error compounding and achieves state-of-the-art results by 4.7% improvement over baseline. |
Unified Speech-Text Pre-training for Speech Translation and Recognition (2022.acl-long)
Copied to clipboard
Yun Tang, Hongyu Gong, Ning Dong, Changhan Wang, Wei-Ning Hsu, Jiatao Gu, Alexei Baevski, Xian Li, Abdelrahman Mohamed, Michael Auli, Juan Pino
| Challenge: | Existing methods to pre-train speech and text use unlabeled data to learn universal feature representations. |
| Approach: | They propose a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. |
| Outcome: | The proposed method achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the MuST-C speech translation dataset and comparable WERs to wav2vec 2.0 on the Librispeech speech recognition task. |
Fine-Tuning a Pre-Trained Wav2Vec2 Model for Automatic Speech Recognition- Experiments with De Zahrar Sproche (2024.lrec-main)
Copied to clipboard
| Challenge: | Developing semi-automatic methods of transcription and annotation based on small amounts of annotated data would free field linguists to focus on tasks that are linguistically and relationally significant during fieldwork. |
| Approach: | They propose to use a pre-trained model to tune a generic pre-trainer model to reduce the transcription workload of field linguists. |
| Outcome: | The proposed system reduces the transcription workload of field linguists by averaging a pre-trained model with a language-specific tuning. |