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SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answering

25 Oct 2019arXiv:1910.11559archive 2025-07-28

Yung-Sung Chuang, Chi-Liang Liu, Hung-Yi Lee, Lin-shan Lee

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.

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Tasks

Language ModelingLanguage ModellingQuestion AnsweringSpeech RecognitionSpoken Language Understanding

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Spoken Language Understanding Spoken-SQuAD SpeechBERT F1 score 71.75 #2 of 4 Archive leaderboard report

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Methods

AdamAttentionAttention DropoutBERTDense ConnectionsDropoutLayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionResidual ConnectionSoftmaxWeight DecayWordPiece

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