SpeechNet: A Universal Modularized Model for Speech Processing Tasks

By Yi-Chen Chen, Po-Han Chi, Shu-Wen Yang, Kai-Wei Chang, Jheng-hao Lin, Sung-Feng Huang, Da-Rong Liu, Chi-Liang Liu, Cheng-Kuang Lee, Hung-yi Lee. 2021

19 citations recorded by the source corpus.

There is a wide variety of speech processing tasks ranging from extracting content information from speech signals to generating speech signals. For different tasks, model networks are usually designed and tuned separately. If a universal model can perform multiple speech processing tasks, some tasks might be improved with the related abilities learned from other tasks. The multi-task learning of a wide variety of speech processing tasks with a universal model has not been studied. This paper proposes a universal modularized model, SpeechNet, which treats all speech processing tasks into a speech/text input and speech/text output format. We select five essential speech processing tasks for multi-task learning experiments with SpeechNet. We show that SpeechNet learns all of the above tasks, and we further analyze which tasks can be improved by other tasks. SpeechNet is modularized and flexible for incorporating more modules, tasks, or training approaches in the future. We release the code and experimental settings to facilitate the research of modularized universal models and multi-task learning of speech processing tasks.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jinyu Li
2. Yu Wu
3. Shujie Liu
4. Long Zhou
5. Junyi Ao
6. Xu Tan
7. Yunfei Chu
8. Furu Wei

Citations in the corpus

• VioLA: Unified Codec Language Models for Speech Recognition, Synthesis, and Translation (2023, neutral): “The primary end-to-end model structures used for ASR (Li, 2022; Prab-havalkar et al., 2023) are connectionist temporal classification (CTC) (Graves et al., 2006), attention-based encoder-decoder network (AED) (Chiu et al., 2018), and transducer networks with RNN or Trans-former (Chen et al., 2021a).”
• SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2021, methodology): “, 2020), which leverages ASR model and TTS model to build a closed-loop machine speech chain and allows us to train model on the concatenation of both labeled and unlabeled data, and SpeechNet (Chen et al., 2021), which designs a universal modularized model to perform multiple speech processing task”
• LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT (2023, background): “The second group, such as SpeechT5 (Ao et al., 2022) and SpeechNet (Chen et al., 2021b), can perform various speech tasks with a single model, but they need to employ modal-specific pre-nets and post-nets to handle different input/output modalities.”
• Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models (2023, methodology): “Existing representative audio-language multi-tasks language models, such as SpeechNet (Chen et al., 2021), SpeechT5 (Ao et al., 2021), VIOLA (Wang et al., 2023d), Whisper (Radford et al., 2023) and Pengi (Deshmukh et al., 2023) are limited to processing specific audio types, such as human speech or ”
• Qwen2-Audio Technical Report (2024, background): “…Qwen2-Audio Figure 1: Performance of Qwen2-Audio, Qwen-Audio and previous top-tiers from LALMs such as SpeechT5 (Ao et al., 2021), SpeechNet (Chen et al., 2021), SpeechLLaMA (Wu et al., 2023a), SALMONN (Tang et al., 2024), Whisper (Radford et al., 2023) Pengi (Deshmukh et al., 2023), and…”