Search Results for author:
Found 12 papers, 4 papers with code
Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval
However, the biasing mechanism is typically based on a cross-attention module between the audio and a catalogue of biasing entries, which means computational complexity can pose severe practical limitations on the size of the biasing catalogue and consequently on accuracy improvements.
Approximate Nearest Neighbour Phrase Mining for Contextual Speech Recognition
By including approximate nearest neighbour phrases (ANN-P) in the context list, we encourage the learned representation to disambiguate between similar, but not identical, biasing phrases.
Variable Attention Masking for Configurable Transformer Transducer Speech Recognition
This work studies the use of attention masking in transformer transducer based speech recognition for building a single configurable model for different deployment scenarios.
Optimizing Bilingual Neural Transducer with Synthetic Code-switching Text Generation
Code-switching describes the practice of using more than one language in the same sentence.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
SLURP: A Spoken Language Understanding Resource Package
Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications.
Ranked #3 on
Slot Filling
on SLURP
(using extra training data)
Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview
We present a structured overview of adaptation algorithms for neural network-based speech recognition, considering both hybrid hidden Markov model / neural network systems and end-to-end neural network systems, with a focus on speaker adaptation, domain adaptation, and accent adaptation.
Multi-task self-supervised learning for Robust Speech Recognition
We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks.
Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery
We find GLCM-based loss to result in images with higher pixelwise accuracy and better perceptual scores.
Benchmarking Natural Language Understanding Services for building Conversational Agents
We have recently seen the emergence of several publicly available Natural Language Understanding (NLU) toolkits, which map user utterances to structured, but more abstract, Dialogue Act (DA) or Intent specifications, while making this process accessible to the lay developer.
Multi-Modal Sequence Fusion via Recursive Attention for Emotion Recognition
Natural human communication is nuanced and inherently multi-modal.
Differentiable Pooling for Unsupervised Acoustic Model Adaptation
We present a deep neural network (DNN) acoustic model that includes parametrised and differentiable pooling operators.
Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
This work presents a broad study on the adaptation of neural network acoustic models by means of learning hidden unit contributions (LHUC) -- a method that linearly re-combines hidden units in a speaker- or environment-dependent manner using small amounts of unsupervised adaptation data.
