AudioPaLM: A Large Language Model That Can Speak and Listen
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Cited by 32 in LLM reasoning, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt. We release examples of our method at https://google-research.github.io/seanet/audiopalm/examples
Key facts. Cited by 333 papers in the map (data through 7 Oct 2026). Builds on 62 of its 78 references that are in the map. First posted on arXiv 22 Jun 2023.
Builds on
62 of 78 references.
References · 62
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- 2023Neural Codec Language Models are Zero-Shot Text to Speech Synthesizerscited by 737
- 2022High Fidelity Neural Audio Compressioncited by 673
- 2022AudioLM: a Language Modeling Approach to Audio Generationcited by 611
- 2020MLS: A Large-Scale Multilingual Dataset for Speech Researchcited by 467
- 2022FLEURS: Few-shot Learning Evaluation of Universal Representations of Speechcited by 458
- 2021VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretationcited by 455
- 2023MusicLM: Generating Music From Textcited by 442
- 2021W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Trainingcited by 340
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- 2020DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressorscited by 235
- 2023Google USM: Scaling Automatic Speech Recognition Beyond 100 Languagescited by 228
- 2021VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modelingcited by 187
- 2022Scaling Up Models and Data with textttt5x and textttseqiocited by 187
- 2023Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervisioncited by 167
- 2023Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modelingcited by 166
- 2022Self-supervised Learning with Random-projection Quantizer for Speech Recognitioncited by 158
- 2021Direct speech-to-speech translation with discrete unitscited by 148
- 2018When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?cited by 125
- 2021Textless Speech-to-Speech Translation on Real Datacited by 121
- 2019Direct speech-to-speech translation with a sequence-to-sequence modelcited by 118
- 2023SoundStorm: Efficient Parallel Audio Generationcited by 111
- 2022mSLAM: Massively multilingual joint pre-training for speech and textcited by 108
- 2023Textually Pretrained Speech Language Modelscited by 99
50 most cited of 62 All 62 (JSON)
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Citing papers per year: 2023: 42; 2024: 134; 2025: 98; 2026: 59.
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- 2023A Survey on Multimodal Large Language Modelscited by 898
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- 2023Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Modelscited by 591
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