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Found 7 papers, 1 papers with code
Exploring Methods for the Automatic Detection of Errors in Manual Transcription
Quality of data plays an important role in most deep learning tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Wav2vec-C: A Self-supervised Model for Speech Representation Learning
However, the quantization process is regularized by an additional consistency network that learns to reconstruct the input features to the wav2vec 2. 0 network from the quantized representations in a way similar to a VQ-VAE model.
Radically Old Way of Computing Spectra: Applications in End-to-End ASR
We propose a technique to compute spectrograms using Frequency Domain Linear Prediction (FDLP) that uses all-pole models to fit the squared Hilbert envelope of speech in different frequency sub-bands.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Importance of Different Temporal Modulations of Speech: A Tale of Two Perspectives
How important are different temporal speech modulations for speech recognition?
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Blind Signal Dereverberation for Machine Speech Recognition
We present a method to remove unknown convolutive noise introduced to speech by reverberations of recording environments, utilizing some amount of training speech data from the reverberant environment, and any available non-reverberant speech data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Stabilized training of joint energy-based models and their practical applications
The JEM training relies on "positive examples" (i. e. examples from the training data set) as well as on "negative examples", which are samples from the modeled distribution $p(x)$ generated by means of Stochastic Gradient Langevin Dynamics (SGLD).
Self-supervised Learning with Speech Modulation Dropout
We show that training a multi-headed self-attention-based deep network to predict deleted, information-dense 2-8 Hz speech modulations over a 1. 5-second section of a speech utterance is an effective way to make machines learn to extract speech modulations using time-domain contextual information.
