SpeechBERT: An audio-and-text jointly learned language model for end-to-end spoken question answering
Journal
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Journal Volume
2020-October
Pages
4168-4172
Date Issued
2020
Author(s)
Abstract
While various end-to-end models for spoken language understanding tasks have been explored recently, this paper is probably the first known attempt to challenge the very difficult task of end-to-end spoken question answering (SQA). Learning from the very successful BERT model for various text processing tasks, here we proposed an audio-and-text jointly learned SpeechBERT model. This model outperformed the conventional approach of cascading ASR with the following text question answering (TQA) model on datasets including ASR errors in answer spans, because the end-to-end model was shown to be able to extract information out of audio data before ASR produced errors. When ensembling the proposed end-to-end model with the cascade architecture, even better performance was achieved. In addition to the potential of end-to-end SQA, the SpeechBERT can also be considered for many other spoken language understanding tasks just as BERT for many text processing tasks. Copyright ? 2020 ISCA
Subjects
Text processing; Audio data; Cascade architecture; Conventional approach; End-to-end models; Extract informations; Language model; Question Answering; Spoken language understanding; Speech communication
SDGs
Type
conference paper