Search Results for author:
Found 36 papers, 10 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.
Parameter Efficient Finetuning for Speech Emotion Recognition and Domain Adaptation
Foundation models have shown superior performance for speech emotion recognition (SER).
FastInject: Injecting Unpaired Text Data into CTC-based ASR training
Recently, connectionist temporal classification (CTC)-based end-to-end (E2E) automatic speech recognition (ASR) models have achieved impressive results, especially with the development of self-supervised learning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Label-Synchronous Neural Transducer for Adaptable Online E2E Speech Recognition
An Auto-regressive Integrate-and-Fire (AIF) mechanism is proposed to generate the label-level encoder representation while retaining low latency operation that can be used for streaming.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Speech-based Slot Filling using Large Language Models
Recently, advancements in large language models (LLMs) have shown an unprecedented ability across various language tasks.
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation
Human annotator simulation (HAS) serves as a cost-effective substitute for human evaluation such as data annotation and system assessment.
Decoupled Structure for Improved Adaptability of End-to-End Models
Although end-to-end (E2E) trainable automatic speech recognition (ASR) has shown great success by jointly learning acoustic and linguistic information, it still suffers from the effect of domain shifts, thus limiting potential applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Integrating Emotion Recognition with Speech Recognition and Speaker Diarisation for Conversations
Two metrics are proposed to evaluate AER performance with automatic segmentation based on time-weighted emotion and speaker classification errors.
Label-Synchronous Neural Transducer for End-to-End ASR
Hence blank tokens are no longer needed and the prediction network can be easily adapted using text data.
Knowledge-Aware Audio-Grounded Generative Slot Filling for Limited Annotated Data
In this work, we propose a Knowledge-Aware Audio-Grounded generative slot-filling framework, termed KA2G, that focuses on few-shot and zero-shot slot filling for ToD with speech input.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression
In automatic emotion recognition (AER), labels assigned by different human annotators to the same utterance are often inconsistent due to the inherent complexity of emotion and the subjectivity of perception.
Can Contextual Biasing Remain Effective with Whisper and GPT-2?
End-to-end automatic speech recognition (ASR) and large language models, such as Whisper and GPT-2, have recently been scaled to use vast amounts of training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Self-supervised representations in speech-based depression detection
This paper proposes handling training data sparsity in speech-based automatic depression detection (SDD) using foundation models pre-trained with self-supervised learning (SSL).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Self-Supervised Learning-Based Source Separation for Meeting Data
In this paper, seven SSL models were compared on both simulated and real-world corpora.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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
Adaptable End-to-End ASR Models using Replaceable Internal LMs and Residual Softmax
End-to-end (E2E) automatic speech recognition (ASR) implicitly learns the token sequence distribution of paired audio-transcript training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Distribution-based Emotion Recognition in Conversation
Automatic emotion recognition in conversation (ERC) is crucial for emotion-aware conversational artificial intelligence.
Biased Self-supervised learning for ASR
Furthermore, this paper proposes a variant of MPPT that allows low-footprint streaming models to be trained effectively by computing the MPPT loss on masked and unmasked frames.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-end Spoken Language Understanding with Tree-constrained Pointer Generator
Specifically, a tree-constrained pointer generator (TCPGen), a powerful and efficient biasing model component, is studied, which leverages a slot shortlist with corresponding entities to extract biasing lists.
Tandem Multitask Training of Speaker Diarisation and Speech Recognition for Meeting Transcription
Self-supervised-learning-based pre-trained models for speech data, such as Wav2Vec 2. 0 (W2V2), have become the backbone of many speech tasks.
Tree-constrained Pointer Generator with Graph Neural Network Encodings for Contextual Speech Recognition
Incorporating biasing words obtained as contextual knowledge is critical for many automatic speech recognition (ASR) applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors
In this paper, a novel Bayesian training loss based on per-utterance Dirichlet prior distributions is proposed for verbal emotion recognition, which models the uncertainty in one-hot labels created when human annotators assign the same utterance to different emotion classes.
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
Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition
Contextual knowledge is important for real-world automatic speech recognition (ASR) applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition
Furthermore, on the AMI corpus, the proposed conversion for language prior probabilities enables BERT to obtain an extra 3% relative WERR, and the combination of BERT, GPT and GPT-2 results in further improvements.
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
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
A Distributed Optimisation Framework Combining Natural Gradient with Hessian-Free for Discriminative Sequence Training
This paper presents a novel natural gradient and Hessian-free (NGHF) optimisation framework for neural network training that can operate efficiently in a distributed manner.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Emotion recognition by fusing time synchronous and time asynchronous representations
In this paper, a novel two-branch neural network model structure is proposed for multimodal emotion recognition, which consists of a time synchronous branch (TSB) and a time asynchronous branch (TAB).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
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
Improved Large-margin Softmax Loss for Speaker Diarisation
Speaker diarisation systems nowadays use embeddings generated from speech segments in a bottleneck layer, which are needed to be discriminative for unseen speakers.
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
Sequence Training of DNN Acoustic Models With Natural Gradient
Deep Neural Network (DNN) acoustic models often use discriminative sequence training that optimises an objective function that better approximates the word error rate (WER) than frame-based training.
Very Deep Convolutional Neural Networks for Robust Speech Recognition
On the Aurora 4 task, the very deep CNN achieves a WER of 8. 81%, further 7. 99% with auxiliary feature joint training, and 7. 09% with LSTM-RNN joint decoding.
