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Talent Scout

Y

Yuxuan Lou

National University of Singapore

K

Kai Yang

Shanghai Jiao Tong University

Y

Yang You

National University of Singapore

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References (23)

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2025Qingkai Fang, Yan Zhou et al.
[2]
Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs
2025Abdelrahman Abouelenin, Atabak Ashfaq et al.
[3]
Seamless communication
2024Rick Farrell
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Moshi: a speech-text foundation model for real-time dialogue
2024Alexandre D'efossez, Laurent Mazar'e et al.
[5]
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts
2024Xi Victoria Lin, Akshat Shrivastava et al.
[6]
MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models
2024Leyang Shen, Gongwei Chen et al.
[7]
Libriheavy: A 50,000 Hours ASR Corpus with Punctuation Casing and Context
2023Wei Kang, Xiaoyu Yang et al.
[8]
Prompting Large Language Models with Speech Recognition Abilities
2023Yassir Fathullah, Chunyang Wu et al.
[9]
Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLM
2023Eliya Nachmani, Alon Levkovitch et al.
[10]
Textually Pretrained Speech Language Models
2023Michael Hassid, Tal Remez et al.
[11]
Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints
2022Aran Komatsuzaki, J. Puigcerver et al.
[12]
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
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[13]
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[16]
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Showing 20 of 23 references

Founder's Pitch

"MoST integrates speech and text processing into an efficient open-source modality-aware language model, outpacing existing solutions in seamless interaction tasks."

Multimodal AIScore: 8View PDF ↗

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Why It Matters

This research is important because it addresses the gap in efficiently integrating speech and text modalities within a single AI model, which is crucial for improving human-computer interaction interfaces and advancing AI-driven communication tools.

Product Angle

To productize this research, develop a software platform offering advanced speech-text integration services, suitable for industries like customer service, edtech, and media. This platform could be licensed to businesses as a SaaS for enhancing their AI-driven communication tools.

Disruption

MoST could replace or enhance current unimodal or less efficient multimodal models in industries relying on conversational AI, such as virtual assistance, automated customer service, and content creation tools.

Product Opportunity

The demand for conversational AI tools in customer service and virtual assistants generates a large market. Potential clients include enterprises seeking to improve user engagement and interaction efficiency through reliable multimodal AI solutions.

Use Case Idea

Develop a virtual assistant with superior speech understanding and text generation capabilities, enabling more natural interaction through fluent dialogue management and real-time transcription services.

Science

The paper introduces MoST, a model that uses a Modality-Aware Mixture of Experts (MAMoE) architecture to process speech and text. This architecture includes modality-specific expert groups for handling distinct input types and shared experts for cross-modal interactions, which are determined by a specialized routing mechanism based on token modalities.

Method & Eval

MoST was rigorously tested across multiple benchmarks including ASR, TTS, and spoken question answering, consistently outperforming comparable models. The use of publicly available datasets ensures reproducibility and accessibility for further development.

Caveats

Possible limitations include the complexity of the architecture which might impact scalability in commercial deployments, and the performance may vary with non-standard dialects or languages not represented in the training data.

Author Intelligence

Yuxuan Lou

LEAD
National University of Singapore
yuxuanlou@u.nus.edu

Kai Yang

Shanghai Jiao Tong University
icarus1411@sjtu.edu.cn

Yang You

National University of Singapore
youy@comp.nus.edu.sg