VoxtLM: Unified Decoder-Only Models for Consolidating Speech Recognition, Synthesis and Speech, Text Continuation Tasks

By Soumi Maiti, Yifan Peng, Shukjae Choi, Jee-weon Jung, Xuankai Chang, Shinji Watanabe. 2023

99 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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Citations in the corpus

UniAudio: An Audio Foundation Model Toward Universal Audio Generation (2023, background): “Several recent works are able to handle up to four audio generation tasks, but most of them did not take the universality with respect to tasks as a core design objective Wang et al. (2023c); Maiti et al. (2023).”
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models (2023, methodology): “For visual modality, GPT4 (OpenAI, 2023), Flamingo (Alayrac et al., 2022), Kosmos (Peng et al., 2023), BLIP (Li et al., 2022), Shikra (Chen et al., 2023), Emu (Sun et al., 2023) and Qwen-VL (Bai et al., 2023) have proposed different integration method to enable image understanding or generation…”