Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community, but have not received as much attention as lower-level tasks like speech and speaker recognition. In this work, we introduce several new annotated SLU benchmark tasks based on freely available speech data, which complement existing benchmarks and address gaps in the SLU evaluation landscape. We contribute four tasks: question answering and summarization involve inference over longer speech sequences; named entity localization addresses the speech-specific task of locating the targeted content in the signal; dialog act classification identifies the function of a given speech utterance. In order to facilitate the development of SLU models that leverage the success of pre-trained speech representations, we will release a new benchmark suite, including for each task (i) curated annotations for a relatively small fine-tuning set, (ii) reproducible pipeline (speech recognizer + text model) and end-to-end baseline models and evaluation metrics, (iii) baseline model performance in various types of systems for easy comparisons. We present the details of data collection and annotation and the performance of the baseline models. We also analyze the sensitivity of pipeline models’ performance to the speech recognition accuracy, using more than 20 publicly availablespeech recognition models.

Spoken language understanding (SLU) tasks involve mapping from speech signals to semantic labels. Given the complexity of such tasks, good performance is expected to require large labeled datasets, which are difficult to collect for each new task and domain. However, recent advances in self-supervised speech representations have made it feasible to consider learning SLU models with limited labeled data. In this work, we focus on low-resource spoken named entity recognition (NER) and address the question: Beyond self-supervised pre-training, how can we use external speech and/or text data that are not annotated for the task? We consider self-training, knowledge distillation, and transfer learning for end-to-end (E2E) and pipeline (speech recognition followed by text NER) approaches. We find that several of these approaches improve performance in resource-constrained settings beyond the benefits from pre-trained representations. Compared to prior work, we find relative improvements in F1 of up to 16%. While the best baseline model is a pipeline approach, the best performance using external data is ultimately achieved by an E2E model. We provide detailed comparisons and analyses, developing insights on, for example, the effects of leveraging external data on (i) different categories of NER errors and (ii) the switch in performance trends between pipeline and E2E models.