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arxiv: 2309.07937 · v3 · pith:6D4SSQS4 · submitted 2023-09-14 · eess.AS · cs.LG· cs.SD

Voxtlm: unified decoder-only models for consolidating speech recognition/synthesis and speech/text continuation tasks

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classification eess.AS cs.LGcs.SD
keywords speechvoxtlmmodelrecognitionsynthesistextcontinuationdecoder-only
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We propose a decoder-only language model, VoxtLM, that can perform four tasks: speech recognition, speech synthesis, text generation, and speech continuation. VoxtLM integrates text vocabulary with discrete speech tokens from self-supervised speech features and uses special tokens to enable multitask learning. Compared to a single-task model, VoxtLM exhibits a significant improvement in speech synthesis, with improvements in both speech intelligibility from 28.9 to 5.6 and objective quality from 2.68 to 3.90. VoxtLM also improves speech generation and speech recognition performance over the single-task counterpart. Further, VoxtLM is trained with publicly available data and training recipes and model checkpoints are open-sourced to make fully reproducible work.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Moshi: a speech-text foundation model for real-time dialogue

    eess.AS 2024-09 accept novelty 7.0

    Moshi is the first real-time full-duplex spoken large language model that casts dialogue as speech-to-speech generation using parallel audio streams and an inner monologue of time-aligned text tokens.

  2. Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

    eess.AS 2023-11 unverdicted novelty 6.0

    Qwen-Audio trains a unified model on diverse audio and tasks with hierarchical tags to enable strong zero-shot performance on audio understanding benchmarks and multi-turn audio chat.