Search Results for author:
Found 19 papers, 1 papers with code
Phoneme-aware Encoding for Prefix-tree-based Contextual ASR
While the original TCPGen relies on grapheme-based encoding, we propose extending it with phoneme-aware encoding to better recognize words of unusual pronunciations.
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions
Recent studies leverage large language models with multi-tasking capabilities, using natural language prompts to guide the model's behavior and surpassing performance of task-specific models.
Ranked #1 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Decoder-only Architecture for Speech Recognition with CTC Prompts and Text Data Augmentation
Because the decoder architecture is the same as an autoregressive LM, it is simple to enhance the model by leveraging external text data with LM training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Integration of Frame- and Label-synchronous Beam Search for Streaming Encoder-decoder Speech Recognition
Although frame-based models, such as CTC and transducers, have an affinity for streaming automatic speech recognition, their decoding uses no future knowledge, which could lead to incorrect pruning.
Integrating Pretrained ASR and LM to Perform Sequence Generation for Spoken Language Understanding
There has been an increased interest in the integration of pretrained speech recognition (ASR) and language models (LM) into the SLU framework.
Tensor decomposition for minimization of E2E SLU model toward on-device processing
We reduce the model size by applying tensor decomposition to the Conformer and E-Branchformer architectures used in our E2E SLU models.
A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge
Recently there have been efforts to introduce new benchmark tasks for spoken language understanding (SLU), like semantic parsing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge
In the track, we adopt a pipeline approach of ASR and NLU.
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
