Search Results for author:
Found 11 papers, 9 papers with code
Unsupervised ASR via Cross-Lingual Pseudo-Labeling
Here, "unsupervised" means no labeled audio is available for the $\textit{target}$ language.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Pseudo-Labeling for Massively Multilingual Speech Recognition
Semi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual speech recognition systems.
SpeechBrain: A General-Purpose Speech Toolkit
SpeechBrain is an open-source and all-in-one speech toolkit.
Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers
This paper introduces Timers and Such, a new open source dataset of spoken English commands for common voice control use cases involving numbers.
Ranked #4 on
Spoken Language Understanding
on Timers and Such
(using extra training data)
Surprisal-Triggered Conditional Computation with Neural Networks
The surprisal of the input, measured as the negative log-likelihood of the current observation according to the autoregressive model, is used as a measure of input difficulty.
Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models
End-to-end models are an attractive new approach to spoken language understanding (SLU) in which the meaning of an utterance is inferred directly from the raw audio without employing the standard pipeline composed of a separately trained speech recognizer and natural language understanding module.
Ranked #7 on
Spoken Language Understanding
on Snips-SmartLights
Speech Model Pre-training for End-to-End Spoken Language Understanding
Whereas conventional spoken language understanding (SLU) systems map speech to text, and then text to intent, end-to-end SLU systems map speech directly to intent through a single trainable model.
Ranked #15 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
DONUT: CTC-based Query-by-Example Keyword Spotting
Keyword spotting--or wakeword detection--is an essential feature for hands-free operation of modern voice-controlled devices.
Learning from the Syndrome
In this paper, we introduce the syndrome loss, an alternative loss function for neural error-correcting decoders based on a relaxation of the syndrome.
Deep Learning Methods for Improved Decoding of Linear Codes
Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.
Neural Offset Min-Sum Decoding
After describing our method, we compare the performance of the two neural decoding algorithms and show that our method achieves error-correction performance within 0. 1 dB of the multiplicative approach and as much as 1 dB better than traditional belief propagation for the codes under consideration.
