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Dynamic-SUPERB: A Benchmark for Instruction-Tuning Speech Models

Core Concepts
Instruction tuning in speech models is crucial for zero-shot learning, as demonstrated by the Dynamic-SUPERB benchmark.
The article introduces Dynamic-SUPERB, a benchmark for instruction-tuning speech models. It addresses the lack of standardized benchmarks in speech processing and aims to facilitate universal speech models capable of zero-shot learning. The benchmark features 55 evaluation instances across 33 tasks and 22 datasets, covering diverse dimensions in speech processing. Various approaches are proposed to establish benchmark baselines, including BERT-GSLM, Whisper, ImageBind-LLM, Whisper-LLM, and ASR-ChatGPT. Evaluation results show that while baselines perform well on seen tasks, they struggle with unseen ones due to limited comprehension of text instructions. The article emphasizes the importance of community collaboration to enhance the benchmark's diversity and effectiveness. INTRODUCTION Text language models excel in zero-shot capability but lack standardized benchmarks in speech processing. Dynamic-SUPERB aims to address this gap by inviting collaboration for building universal speech models through instruction tuning. TASKS & DATASETS Tasks span dimensions like content, speaker, semantics, degradation, paralinguistics, and audio processing. Each task includes text instructions, speech utterances, and text labels for evaluation. BASELINE FRAMEWORKS Five approaches are proposed: BERT-GSLM, Whisper, ImageBind-LLM, Whisper-LLM, and ASR-ChatGPT. Evaluation results show varying performance across different dimensions on seen tasks. RESULTS Seen Tasks GSLM performs poorly compared to other models across different dimensions. Whisper excels in content dimension while Whisper-LLM dominates most dimensions except content. Unseen Tasks Baseline models struggle with unseen tasks due to limited comprehension of text instructions. Performance declines significantly on unseen tasks compared to seen tasks across all models.
Text language models have shown remarkable zero-shot capability in generalizing to unseen tasks when provided with well-formulated instructions. However existing studies primarily focus on limited or specific tasks. Moreover the lack of standardized benchmarks hinders a fair comparison across different approaches. We present Dynamic-SUPERB a benchmark designed for building universal speech models capable of leveraging instruction tuning to perform multiple tasks in a zero-shot fashion. To achieve comprehensive coverage of diverse speech tasks and harness instruction tuning we invite the community to collaborate and contribute facilitating the dynamic growth of the benchmark. To initiate Dynamic-SUPERB features 55 evaluation instances by combining 33 tasks and 22 datasets. This spans a broad spectrum of dimensions providing a comprehensive platform for evaluation. Additionally we propose several approaches to establish benchmark baselines. These include the utilization of speech models text language models and multimodal encoder. Evaluation results indicate that while these baselines perform reasonably on seen tasks they struggle with unseen ones. We release all materials to the public and welcome researchers to collaborate on the project advancing technologies in the field together1. Each task consists of at least three components: speech utterance instruction and label. We sourced it from widely-used corpora such as LibriSpeech LJSpeech and VCTK . Depending on the task specification there may be more than one utterance . For example in speaker verification two utterances are involved to determine whether they are produced by the same speaker . For instructions we chose to use text format rather than spoken format . Spoken instructions with varying content speaker characteristics and prosody are more complex than text-based ones . Text instructions serve as an intermediary bridging text and spoken instruction tuning . To generate various instructions we initially created some basic ones manually and then used ChatGPT to rephrase them and generate additional variations . This results in each task containing approximately 10 to 30 different types of instructions . We propose five approaches to establish baselines in Dynamic-SUPERB . We integrated text embeddings from BERT into the generative spoken language model GSLM enabling operations on both speech and text . We also adapted Whisper which was primarily designed for speech recognition involving both speech and text ,to instruction tuning . Besides modifying speech models we integrated speech representations from either Whisper or ImageBind into LLaMA a prevalent large language model LLM . We also utilize Whisper ASR ,and ChatGPT ,to build a concatenative system ASR - ChatGPT . While instruction tuning is increasingly popular for enabling zero-shot applications in NLP it remains underexplored in speech processing . This paper presents Dynamic-SUPERB ,the first dynamic collaborative benchmark for instruction tuning in speech models offering a comprehensive exploration across diverse dimensions .
"We believe that instruction tuning is a pivotal step towards universal speech models." "Dynamic-SUPERB seeks to dynamically expand its task variety through community collaboration."

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by Chien-yu Hua... at 03-25-2024

Deeper Inquiries

How can community collaboration enhance diversity within benchmarks like Dynamic-SUPERB

Community collaboration plays a crucial role in enhancing diversity within benchmarks like Dynamic-SUPERB. By inviting researchers and experts from various backgrounds to contribute, the benchmark can benefit from a wide range of perspectives, leading to the inclusion of tasks that cover different dimensions, complexities, and nuances in speech processing. Diverse contributions ensure that the benchmark reflects a more comprehensive set of challenges and scenarios faced in real-world applications. Additionally, community involvement fosters innovation by introducing novel tasks and approaches that may not have been considered otherwise. This collaborative effort helps expand the scope of evaluation criteria and promotes inclusivity by accommodating different research interests and expertise.

What challenges might arise when transitioning baseline performance from seen tasks to unseen tasks

Transitioning baseline performance from seen tasks to unseen tasks poses several challenges for models trained on specific instructions or datasets. One significant challenge is related to generalization capabilities; models optimized for seen tasks may struggle with unfamiliar instructions or task variations present in unseen data. The discrepancy between training data distribution and unseen task requirements can lead to poor performance due to overfitting or lack of adaptability. Moreover, unseen tasks may introduce new patterns or complexities not encountered during training, requiring models to extrapolate knowledge effectively. Another challenge lies in handling diverse instruction formats; while models may excel at recognizing patterns within familiar instructions, they could falter when presented with novel linguistic structures or semantic variations common in unseen instructions/tasks. Adapting model responses based on these differences becomes critical but challenging without prior exposure during training. Furthermore, limited exposure to diverse task types during training can hinder model robustness when confronted with unforeseen scenarios during testing on unseen tasks. Models need sufficient exposure to varied contexts and prompts for effective adaptation across different domains.

How can pre-training on large-scale text data impact model performance when handling unseen instructions/tasks

Pre-training on large-scale text data significantly impacts model performance when handling unseen instructions/tasks by enhancing language understanding capabilities through exposure to diverse linguistic patterns and semantics. Models pre-trained on extensive textual corpora develop robust representations that capture intricate relationships between words/phrases, enabling them to comprehend complex sentence structures better. This pre-training facilitates improved transfer learning abilities as models leverage learned features for interpreting new instructions efficiently even if they differ substantially from those encountered during training phases. The broad contextual knowledge gained through pre-training equips models with a rich vocabulary base and syntactic awareness necessary for generating coherent responses across various instruction formats commonly found in both seen and unforeseen settings. Moreover, pre-trained models exhibit higher adaptability towards previously unencountered prompts due to their capacity for semantic abstraction derived from extensive text-based learning experiences. This adaptability enables them to generalize well beyond familiar contexts by inferring underlying patterns rather than relying solely on memorized responses—a key advantage when tackling unknown instructional paradigms typical of unseen tasks.