Reference summaries for abstractive speech summarization require human annotation, which can be performed by listening to an audio recording or by reading textual transcripts of the recording.In this paper, we examine whether summaries based on annotators listening to the recordings differ from those based on annotators reading transcripts.Using existing intrinsic evaluation based on human evaluation, automatic metrics, LLM-based evaluation, and a retrieval-based reference-free method.We find that summaries are indeed different based on the source modality, and that speechbased summaries are more factually consistent and information-selective than transcript-based summaries.Meanwhile, transcript-based summaries are impacted by recognition errors in the source, and expert-written summaries are more informative and reliable.We make all the collected data and analysis code public 1 to facilitate the reproduction of our work and advance research in this area.

Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling. High-dimensional speech features such as spectrograms are often used as the input for the subsequent model. However, they can still be redundant. Recent investigations proposed the use of discrete speech units derived from self-supervised learning representations, which significantly compresses the size of speech data. Applying various methods, such as de-duplication and subword modeling, can further compress the speech sequence length. Hence, training time is significantly reduced while retaining notable performance. In this study, we undertake a comprehensive and systematic exploration into the application of discrete units within end-to-end speech processing models. Experiments on 12 automatic speech recognition, 3 speech translation, and 1 spoken language understanding corpora demonstrate that discrete units achieve reasonably good results in almost all the settings. Our configurations and trained models are released in ESPnet to foster future research efforts.

Text language models have shown remarkable zero-shot capability in generalizing to unseen tasks when provided with well-formulated instructions. However, existing studies in speech processing primarily focus on limited or specific tasks. Moreover, the lack of standardized benchmarks hinders a fair comparison across different approaches. Thus, 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 the 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.

Abstractive speech summarization (SSUM) aims to generate humanlike summaries from speech. Given variations in information captured and phrasing, recordings can be summarized in multiple ways. Therefore, it is more reasonable to consider a probabilistic distribution of all potential summaries rather than a single summary. However, conventional SSUM models are mostly trained and evaluated with a single ground-truth (GT) human-annotated deterministic summary for every recording. Generating multiple human references would be ideal to better represent the distribution statistically, but is impractical because annotation is expensive. We tackle this challenge by proposing AugSumm, a method to leverage large language models (LLMs) as a proxy for human annotators to generate augmented summaries for training and evaluation. First, we explore prompting strategies to generate synthetic summaries from ChatGPT. We validate the quality of synthetic summaries using multiple metrics including human evaluation, where we find that summaries generated using AugSumm are perceived as more valid to humans. Second, we develop methods to utilize synthetic summaries in training and evaluation. Experiments on How2 demonstrate that pre-training on synthetic summaries and fine-tuning on GT summaries improves ROUGE-L by 1 point on both GT and AugSumm-based test sets. AugSumm summaries are available at https://github.com/Jungjee/AugSumm.

Siddhant Arora, Hayato Futami, Jee-weon Jung, Yifan Peng, Roshan Sharma, Yosuke Kashiwagi, Emiru Tsunoo, Karen Livescu, Shinji Watanabe. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Suwon Shon, Siddhant Arora, Chyi-Jiunn Lin, Ankita Pasad, Felix Wu, Roshan S Sharma, Wei-Lun Wu, Hung-yi Lee, Karen Livescu, Shinji Watanabe. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Pre-training speech models on large volumes of data has achieved remarkable success. OpenAI Whisper is a multilingual multitask model trained on 680k hours of supervised speech data. It generalizes well to various speech recognition and translation benchmarks even in a zero-shot setup. However, the full pipeline for developing such models (from data collection to training) is not publicly accessible, which makes it difficult for researchers to further improve its performance and address training-related issues such as efficiency, robustness, fairness, and bias. This work presents an Open Whisper-style Speech Model (OWSM), which reproduces Whisperstyle training using an open-source toolkit and publicly available data. OWSM even supports more translation directions and can be more efficient to train. We will publicly release all scripts used for data preparation, training, inference, and scoring as well as pretrained models and training logs to promote open science.11https://github.com/espnet/espnet

Speech summarization has garnered significant interest and progressed rapidly over the past few years. In particular, end-to-end models have recently emerged as a competitive alternative to cascade systems for abstractive video summarization. This paper aims to establish progress in this rapidly evolving research field, by introducing ESPNet-SUMM, a new open-source toolkit that facilitates a comprehensive comparison of end-to-end and cascade speech summarization models on 4 different speech summarization tasks spanning diverse applications. Experiments demonstrate that end-to-end models perform better for larger corpora with shorter inputs. This work also introduces Interview, the largest public open-domain multiparty interview corpus with $4400 \mathrm{~h}$ of conversations between radio hosts and guests. Finally, this work explores the use of multiple datasets to improve end-to-end summarization, and experiments demonstrate the benefit of multi-style training over fine-tuning. 1

Speech summarization requires processing several minute-long speech sequences to allow exploiting the whole context of a spoken document. A conventional approach is a cascade of automatic speech recognition (ASR) and text summarization (TS). However, the cascade systems are sensitive to ASR errors. Moreover, the cascade system cannot be optimized for input speech and utilize para-linguistic information. Recently, there has been an increased interest in end-to-end (E2E) approaches optimized to output summaries directly from speech. Such systems can thus mitigate the ASR errors of cascade approaches. However, E2E speech summarization requires massive computational resources because it needs to encode long speech sequences. We propose a speech summarization system that enables E2E summarization from 100 seconds, which is the limit of the conventional method, to up to 10 minutes (i.e., the duration of typical instructional videos on YouTube). However, the modeling capability of this model for minute-long speech sequences is weaker than the conventional approach. We thus exploit auxiliary text information from ASR transcriptions to improve the modeling capabilities. The resultant system consists of a dual speech/text encoder decoder-based summarization system. We perform experiments on the How2 dataset showing the proposed system improved METEOR scores by up to 2.7 points by fully exploiting the long spoken documents.