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Found 11 papers, 0 papers with code
Streaming Joint Speech Recognition and Disfluency Detection
In this study, we propose Transformer-based encoder-decoder models that jointly solve speech recognition and disfluency detection, which work in a streaming manner.
Residual Language Model for End-to-end Speech Recognition
In this paper, we propose a simple external LM fusion method for domain adaptation, which considers the internal LM estimation in its training.
Joint Speech Recognition and Audio Captioning
A major hurdle in evaluating our proposed approach is the lack of labeled audio datasets with both speech transcriptions and audio captions.
Run-and-back stitch search: novel block synchronous decoding for streaming encoder-decoder ASR
To this end, we propose a novel blockwise synchronous decoding algorithm with a hybrid approach that combines endpoint prediction and endpoint post-determination.
Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models
In order to optimize the DNN-based SE model in terms of the character error rate (CER), which is one of the metric to evaluate the ASR system and generally non-differentiable, our method uses two DNNs: one for speech processing and one for mimicking the output CERs derived through an acoustic model (AM).
Data Augmentation Methods for End-to-end Speech Recognition on Distant-Talk Scenarios
Although end-to-end automatic speech recognition (E2E ASR) has achieved great performance in tasks that have numerous paired data, it is still challenging to make E2E ASR robust against noisy and low-resource conditions.
Gaussian Kernelized Self-Attention for Long Sequence Data and Its Application to CTC-based Speech Recognition
Self-attention (SA) based models have recently achieved significant performance improvements in hybrid and end-to-end automatic speech recognition (ASR) systems owing to their flexible context modeling capability.
Streaming Transformer ASR with Blockwise Synchronous Inference
In this paper, we extend block processing towards an entire streaming E2E ASR system without additional training, by introducing a blockwise synchronous decoding process inspired by a neural transducer into the Transformer decoder.
Towards Online End-to-end Transformer Automatic Speech Recognition
In this paper, we extend it towards an entire online E2E ASR system by introducing an online decoding process inspired by monotonic chunkwise attention (MoChA) into the Transformer decoder.
Transformer ASR with Contextual Block Processing
In this paper, we propose a new block processing method for the Transformer encoder by introducing a context-aware inheritance mechanism.
End-to-end Adaptation with Backpropagation through WFST for On-device Speech Recognition System
We convert a pretrained WFST to a trainable neural network and adapt the system to target environments/vocabulary by E2E joint training with an AM.
