SLU for Voice Command in Smart Home: Comparison of Pipeline and End-to-End Approaches

Abstract

Spoken Language Understanding (SLU) is typically performed through automatic speech recognition (ASR) and natural language understanding (NLU) in a pipeline. However, errors at the ASR stage have a negative impact on the NLU performance. Hence, there is a rising interest in End-to-End (E2E) SLU to jointly perform ASR and NLU. Although E2E models have shown superior performance to modular approaches in many NLP tasks, current SLU E2E models have still not definitely superseded pipeline approaches. In this paper, we present a comparison of the pipeline and E2E approaches for the task of voice command in smart homes. Since there are no large non-English domain-specific data sets available, although needed for an E2E model, we tackle the lack of such data by combining Natural Language Generation (NLG) and text-to-speech (TTS) to generate French training data. The trained models were evaluated on voice commands acquired in a real smart home with several speakers. Results show that the E2E approach can reach performances similar to a state-of-the art pipeline SLU despite a higher WER than the pipeline approach. Furthermore, the E2E model can benefit from artificially generated data to exhibit lower Concept Error Rates than the pipeline baseline for slot recognition.