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Found 8 papers, 5 papers with code
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.
Speaker Adaptive Training using Model Agnostic Meta-Learning
Speaker adaptive training (SAT) of neural network acoustic models learns models in a way that makes them more suitable for adaptation to test conditions.
Acoustic Model Adaptation from Raw Waveforms with SincNet
Raw waveform acoustic modelling has recently gained interest due to neural networks' ability to learn feature extraction, and the potential for finding better representations for a given scenario than hand-crafted features.
Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models
Acoustic model adaptation to unseen test recordings aims to reduce the mismatch between training and testing conditions.
Lattice-based lightly-supervised acoustic model training
This text data can be used for lightly supervised training, in which text matching the audio is selected using an existing speech recognition model.
Talking to myself: self-dialogues as data for conversational agents
Conversational agents are gaining popularity with the increasing ubiquity of smart devices.
Learning to adapt: a meta-learning approach for speaker adaptation
The performance of automatic speech recognition systems can be improved by adapting an acoustic model to compensate for the mismatch between training and testing conditions, for example by adapting to unseen speakers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Edina: Building an Open Domain Socialbot with Self-dialogues
We present Edina, the University of Edinburgh's social bot for the Amazon Alexa Prize competition.
