Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Approach 2 Approach 2.1 Architecture 2.1 Architecture 2.2 Multimodal Instruction Finetuning 2.2 Multimodal Instruction Finetuning 2.3 Curriculum Learning with Parameter Efficient Finetuning 2.3 Curriculum Learning with Parameter Efficient Finetuning 3 Experiments 3 Experiments 4 Results 4 Results 4.1 Evaluation of SpeechVerse models 4.1 Evaluation of SpeechVerse models 4.2 Generalization Across Instructions 4.2 Generalization Across Instructions 4.3 Strategies for Improving Performance 4.3 Strategies for Improving Performance 5 Related Work 5 Related Work 6 Conclusion, Limitations, Ethics Statement, and References 6 Conclusion, Limitations, Ethics Statement, and References A Appendix A Appendix A.1 Audio Encoder Pre-training A.1 Audio Encoder Pre-training A.2 Hyper-parameters A.2 Hyper-parameters A.3 Tasks A.3 Tasks 6 Conclusion In this work, we propose SpeechVerse, a multimodal framework that enables LLMs to follow natural language instructions for performing diverse speech processing tasks. Through supervised instruction finetuning and combining representations from frozen pre-trained speech and text foundation models, SpeechVerse achieves strong zero-shot generalization on unseen tasks. Extensive benchmarking against conventional baselines show SpeechVerse’s superiority on 9 out of 11 tasks, demonstrating its formidable instruction following capability. Crucially, SpeechVerse maintains robust performance on out-of-domain datasets, unseen prompts, and even unseen tasks. This highlights the efficacy of our proposed training methodology in imbuing the model with a generalizable skill for mapping text-based instructions to speech processing outputs. Moving forward, we aim to expand SpeechVerse’s capabilities to follow even more complex instructions and generalize to new domains. By separating task specification from model design, SpeechVerse represents a versatile framework that can dynamically adapt to new tasks through natural language without retraining. Limitations While this work demonstrated strong instruction following capabilities for the multitask SpeechVerse model across a variety of tasks, some limitations remain. The study relied on a single underlying LLM architecture (FlanT5) rather than exploring more recent models tailored for instruction following. Additionally, there is a trade-off between generalized capabilities on unseen tasks versus specialized performance on original training tasks that poses challenges for a single multitask model. While the model showed promise in handling diverse unseen tasks, its limitations were not fully characterized across the wide scope of possible instructions and the performance on these unseen tasks is not quantitatively measured. Ethics Statement All speech datasets we use have anonymous speakers. We do not have any access to nor try to create any PII (Personal Identifiable Information) of speakers, and our model neither identifies speakers nor uses speaker embeddings. 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Cieri Christopher, “2000 hub5 english evaluation speech ldc2002s09,” Web Download. Philadelphia: Linguistic Data Consortium, 2002. Authors:
(1) Nilaksh Das, AWS AI Labs, Amazon and Equal Contributions;
(2) Saket Dingliwal, AWS AI Labs, Amazon(skdin@amazon.com);
(3) Srikanth Ronanki, AWS AI Labs, Amazon;
(4) Rohit Paturi, AWS AI Labs, Amazon;
(5) Zhaocheng Huang, AWS AI Labs, Amazon;
(6) Prashant Mathur, AWS AI Labs, Amazon;
(7) Jie Yuan, AWS AI Labs, Amazon;
(8) Dhanush Bekal, AWS AI Labs, Amazon;
(9) Xing Niu, AWS AI Labs, Amazon;
(10) Sai Muralidhar Jayanthi, AWS AI Labs, Amazon;
(11) Xilai Li, AWS AI Labs, Amazon;
(12) Karel Mundnich, AWS AI Labs, Amazon;
(13) Monica Sunkara, AWS AI Labs, Amazon;
(14) Daniel Garcia-Romero, AWS AI Labs, Amazon;
(15) Kyu J. Han, AWS AI Labs, Amazon;
(16) Katrin Kirchhoff, AWS AI Labs, Amazon. Authors: Authors: (1) Nilaksh Das, AWS AI Labs, Amazon and Equal Contributions; (2) Saket Dingliwal, AWS AI Labs, Amazon(skdin@amazon.com); (3) Srikanth Ronanki, AWS AI Labs, Amazon; (4) Rohit Paturi, AWS AI Labs, Amazon; (5) Zhaocheng Huang, AWS AI Labs, Amazon; (6) Prashant Mathur, AWS AI Labs, Amazon; (7) Jie Yuan, AWS AI Labs, Amazon; (8) Dhanush Bekal, AWS AI Labs, Amazon; (9) Xing Niu, AWS AI Labs, Amazon; (10) Sai Muralidhar Jayanthi, AWS AI Labs, Amazon; (11) Xilai Li, AWS AI Labs, Amazon; (12) Karel Mundnich, AWS AI Labs, Amazon; (13) Monica Sunkara, AWS AI Labs, Amazon; (14) Daniel Garcia-Romero, AWS AI Labs, Amazon; (15) Kyu J. Han, AWS AI Labs, Amazon; (16) Katrin Kirchhoff, AWS AI Labs, Amazon. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

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