5 accepted papers
The neural transducer is an end-to-end model for automatic speech recognition (ASR). While the model is well-suited for streaming ASR, the training process remains challenging. During training, the memory requirements may quickly exceed the capacity of state-of-the-art GPUs, limiting batch size and…
This work studies the use of attention masking in transformer transducer based speech recognition for building a single configurable model for different deployment scenarios. We present a comprehensive set of experiments comparing fixed masking, where the same attention mask is applied at every fram…
Data augmentation methods usually apply the same augmentation (or a mix of them) to all the training samples. For example, to perturb data with noise, the noise is sampled from a Normal distribution with a fixed standard deviation, for all samples. We hypothesize that a hard sample with high trainin…
This work presents an event-driven acoustic sensor processing pipeline to power a low-resource voice-activated smart assistant. The pipeline includes four major steps; namely localization, source separation, keyword spotting (KWS) and speaker verification (SV). The pipeline is driven by a front-end…
Recent work on neural networks with probabilistic parameters has shown that parameter uncertainty improves network regularization. Parameter-specific signal-to-noise ratio (SNR) levels derived from parameter distributions were further found to have high correlations with task importance. However, mo…