Search Results for author:
Found 25 papers, 1 papers with code
The USFD Spoken Language Translation System for IWSLT 2014
The USFD primary system incorporates state-of-the-art ASR and MT techniques and gives a BLEU score of 23. 45 and 14. 75 on the English-to-French and English-to-German speech-to-text translation task with the IWSLT 2014 data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Top-down training for neural networks
Interpreting the top layers as a classifier and the lower layers a feature extractor, one can hypothesize that unwanted network convergence may occur when the classifier has overfit with respect to the feature extractor.
An Investigation into the Effectiveness of Enhancement in ASR Training and Test for CHiME-5 Dinner Party Transcription
Despite the strong modeling power of neural network acoustic models, speech enhancement has been shown to deliver additional word error rate improvements if multi-channel data is available.
Action State Update Approach to Dialogue Management
Utterance interpretation is one of the main functions of a dialogue manager, which is the key component of a dialogue system.
On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments
To reduce the influence of reverberation on spatial feature extraction, a dereverberation pre-processing method has been applied to further improve the separation performance.
Time-Domain Speech Extraction with Spatial Information and Multi Speaker Conditioning Mechanism
In this paper, we present a novel multi-channel speech extraction system to simultaneously extract multiple clean individual sources from a mixture in noisy and reverberant environments.
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers
Although the lower layers of a deep neural network learn features which are transferable across datasets, these layers are not transferable within the same dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multiple-hypothesis CTC-based semi-supervised adaptation of end-to-end speech recognition
In this method, multiple automatic speech recognition (ASR) 1-best hypotheses are integrated in the computation of the connectionist temporal classification (CTC) loss function.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Head-synchronous Decoding for Transformer-based Streaming ASR
Online Transformer-based automatic speech recognition (ASR) systems have been extensively studied due to the increasing demand for streaming applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Teacher-Student MixIT for Unsupervised and Semi-supervised Speech Separation
The proposed method first uses mixtures of unseparated sources and the mixture invariant training (MixIT) criterion to train a teacher model.
Towards Handling Unconstrained User Preferences in Dialogue
A user input to a schema-driven dialogue information navigation system, such as venue search, is typically constrained by the underlying database which restricts the user to specify a predefined set of preferences, or slots, corresponding to the database fields.
Monaural source separation: From anechoic to reverberant environments
Impressive progress in neural network-based single-channel speech source separation has been made in recent years.
A study on cross-corpus speech emotion recognition and data augmentation
Models trained on mixed corpora can be more stable in mismatched contexts, and the performance reductions range from 1 to 8% when compared with single corpus models in matched conditions.
Transformer-based Streaming ASR with Cumulative Attention
In this paper, we propose an online attention mechanism, known as cumulative attention (CA), for streaming Transformer-based automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Dialogue Strategy Adaptation to New Action Sets Using Multi-dimensional Modelling
A major bottleneck for building statistical spoken dialogue systems for new domains and applications is the need for large amounts of training data.
On monoaural speech enhancement for automatic recognition of real noisy speech using mixture invariant training
In this paper, we explore an improved framework to train a monoaural neural enhancement model for robust speech recognition.
Speaker Reinforcement Using Target Source Extraction for Robust Automatic Speech Recognition
Improving the accuracy of single-channel automatic speech recognition (ASR) in noisy conditions is challenging.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multiple-hypothesis RNN-T Loss for Unsupervised Fine-tuning and Self-training of Neural Transducer
The multiple-hypothesis approach yields a relative reduction of 3. 3% WER on the CHiME-4's single-channel real noisy evaluation set when compared with the single-hypothesis approach.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Non-autoregressive End-to-end Approaches for Joint Automatic Speech Recognition and Spoken Language Understanding
This paper presents the use of non-autoregressive (NAR) approaches for joint automatic speech recognition (ASR) and spoken language understanding (SLU) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Self-regularised Minimum Latency Training for Streaming Transformer-based Speech Recognition
In previous works, latency was optimised by truncating the online attention weights based on the hard alignments obtained from conventional ASR models, without taking into account the potential loss of ASR accuracy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Adversarial learning of neural user simulators for dialogue policy optimisation
Reinforcement learning based dialogue policies are typically trained in interaction with a user simulator.
A Teacher-Student approach for extracting informative speaker embeddings from speech mixtures
We introduce a monaural neural speaker embeddings extractor that computes an embedding for each speaker present in a speech mixture.
Frame-wise and overlap-robust speaker embeddings for meeting diarization
Using a Teacher-Student training approach we developed a speaker embedding extraction system that outputs embeddings at frame rate.
Evaluating Large Language Models for Document-grounded Response Generation in Information-Seeking Dialogues
Observing that document-grounded response generation via LLMs cannot be adequately assessed by automatic evaluation metrics as they are significantly more verbose, we perform a human evaluation where annotators rate the output of the shared task winning system, the two Chat-GPT variants outputs, and human responses.
Geodesic interpolation of frame-wise speaker embeddings for the diarization of meeting scenarios
We propose a modified teacher-student training for the extraction of frame-wise speaker embeddings that allows for an effective diarization of meeting scenarios containing partially overlapping speech.
