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Found 9 papers, 0 papers with code
Locality enhanced dynamic biasing and sampling strategies for contextual ASR
During training, a list of biasing phrases are selected from a large pool of phrases following a sampling strategy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Consistency Based Unsupervised Self-training For ASR Personalisation
On-device Automatic Speech Recognition (ASR) models trained on speech data of a large population might underperform for individuals unseen during training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
On-Device Speaker Anonymization of Acoustic Embeddings for ASR based onFlexible Location Gradient Reversal Layer
Our paper proposes a privacy-enhancing framework that targets speaker identity anonymization while preserving speech recognition accuracy for our downstream task~-~Automatic Speech Recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition
The benefits of using a distributed approach to speech emotion understanding are supported by the results of cross-corpora analysis experiments.
Towards domain generalisation in ASR with elitist sampling and ensemble knowledge distillation
Knowledge distillation has widely been used for model compression and domain adaptation for speech applications.
Dynamic Kernels and Channel Attention for Low Resource Speaker Verification
Instead, this paper proposes an approach to increase the model resolution capability using attention-based dynamic kernels in a convolutional neural network to adapt the model parameters to be feature-conditioned.
Probing Statistical Representations For End-To-End ASR
End-to-End automatic speech recognition (ASR) models aim to learn a generalised speech representation to perform recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A cross-corpus study on speech emotion recognition
This shows positive information transfer from acted datasets to those with more natural emotions and the benefits from training on different corpora.
Insights on Neural Representations for End-to-End Speech Recognition
This paper analyses and explores the internal dynamics between layers during training with CNN, LSTM and Transformer based approaches using Canonical correlation analysis (CCA) and centered kernel alignment (CKA) for the experiments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
