Log In Sign Up

Label-Synchronous Speech-to-Text Alignment for ASR Using Forward and Backward Transformers

by   Yusuke Kida, et al.

This paper proposes a novel label-synchronous speech-to-text alignment technique for automatic speech recognition (ASR). The speech-to-text alignment is a problem of splitting long audio recordings with un-aligned transcripts into utterance-wise pairs of speech and text. Unlike conventional methods based on frame-synchronous prediction, the proposed method re-defines the speech-to-text alignment as a label-synchronous text mapping problem. This enables an accurate alignment benefiting from the strong inference ability of the state-of-the-art attention-based encoder-decoder models, which cannot be applied to the conventional methods. Two different Transformer models named forward Transformer and backward Transformer are respectively used for estimating an initial and final tokens of a given speech segment based on end-of-sentence prediction with teacher-forcing. Experiments using the corpus of spontaneous Japanese (CSJ) demonstrate that the proposed method provides an accurate utterance-wise alignment, that matches the manually annotated alignment with as few as 0.2 Transformer-based hybrid CTC/Attention ASR model using the aligned speech and text pairs as an additional training data reduces character error rates relatively up to 59.0 conventional alignment method based on connectionist temporal classification model.


page 1

page 2

page 3

page 4


Synchronous Transformers for End-to-End Speech Recognition

For most of the attention-based sequence-to-sequence models, the decoder...

Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept

With the advent of direct models in automatic speech recognition (ASR), ...

A Comparison of Label-Synchronous and Frame-Synchronous End-to-End Models for Speech Recognition

End-to-end models are gaining wider attention in the field of automatic ...

Improving Transformer-based Conversational ASR by Inter-Sentential Attention Mechanism

Transformer-based models have demonstrated their effectiveness in automa...

Iterative pseudo-forced alignment by acoustic CTC loss for self-supervised ASR domain adaptation

High-quality data labeling from specific domains is costly and human tim...

On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR

We propose an on-the-fly data augmentation method for automatic speech r...

Run-and-back stitch search: novel block synchronous decoding for streaming encoder-decoder ASR

A streaming style inference of encoder-decoder automatic speech recognit...