Search Results for author:
Found 18 papers, 4 papers with code
Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation
The evidential uncertainty measure is extended to quantify the uncertainty in emotion distribution estimation.
Efficient Adapter Finetuning for Tail Languages in Streaming Multilingual ASR
Meanwhile, the heterogeneous nature and imbalanced data abundance of different languages may cause performance degradation, leading to asynchronous peak performance for different languages during training, especially on tail ones.
Massive End-to-end Models for Short Search Queries
In this work, we investigate two popular end-to-end automatic speech recognition (ASR) models, namely Connectionist Temporal Classification (CTC) and RNN-Transducer (RNN-T), for offline recognition of voice search queries, with up to 2B model parameters.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Modular Domain Adaptation for Conformer-Based Streaming ASR
Speech data from different domains has distinct acoustic and linguistic characteristics.
Knowledge Distillation from Multiple Foundation Models for End-to-End Speech Recognition
The performance of the student model can be further enhanced when multiple teachers are used jointly, achieving word error rate reductions (WERRs) of 17. 5% and 10. 6%.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Knowledge Distillation for Neural Transducers from Large Self-Supervised Pre-trained Models
Self-supervised pre-training is an effective approach to leveraging a large amount of unlabelled data to reduce word error rates (WERs) of automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition
As end-to-end automatic speech recognition (ASR) models reach promising performance, various downstream tasks rely on good confidence estimators for these systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Combining Frame-Synchronous and Label-Synchronous Systems for Speech Recognition
Commonly used automatic speech recognition (ASR) systems can be classified into frame-synchronous and label-synchronous categories, based on whether the speech is decoded on a per-frame or per-label basis.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction
Confidence scores are very useful for downstream applications of automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Residual Energy-Based Models for End-to-End Speech Recognition
End-to-end models with auto-regressive decoders have shown impressive results for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Learning Word-Level Confidence For Subword End-to-End ASR
We study the problem of word-level confidence estimation in subword-based end-to-end (E2E) models for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech Recognition
For various speech-related tasks, confidence scores from a speech recogniser are a useful measure to assess the quality of transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Discriminative Neural Clustering for Speaker Diarisation
In this paper, we propose Discriminative Neural Clustering (DNC) that formulates data clustering with a maximum number of clusters as a supervised sequence-to-sequence learning problem.
Integrating Source-channel and Attention-based Sequence-to-sequence Models for Speech Recognition
This paper proposes a novel automatic speech recognition (ASR) framework called Integrated Source-Channel and Attention (ISCA) that combines the advantages of traditional systems based on the noisy source-channel model (SC) and end-to-end style systems using attention-based sequence-to-sequence models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Confidence Estimation and Deletion Prediction Using Bidirectional Recurrent Neural Networks
These errors are not accounted for by the standard confidence estimation schemes and are hard to rectify in the upstream and downstream processing.
Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation
The standard approach to mitigate errors made by an automatic speech recognition system is to use confidence scores associated with each predicted word.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Shape and Material from Sound
Hearing an object falling onto the ground, humans can recover rich information including its rough shape, material, and falling height.
Generative Modeling of Audible Shapes for Object Perception
Humans infer rich knowledge of objects from both auditory and visual cues.
