Search Results for author:
Found 13 papers, 1 papers with code
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.
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).
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
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
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
Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR
Performance degradation of an Automatic Speech Recognition (ASR) system is commonly observed when the test acoustic condition is different from training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A practical two-stage training strategy for multi-stream end-to-end speech recognition
The multi-stream paradigm of audio processing, in which several sources are simultaneously considered, has been an active research area for information fusion.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-Stream End-to-End Speech Recognition
Two representative framework have been proposed and discussed, which are Multi-Encoder Multi-Resolution (MEM-Res) framework and Multi-Encoder Multi-Array (MEM-Array) framework, respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Performance Monitoring for End-to-End Speech Recognition
Measuring performance of an automatic speech recognition (ASR) system without ground-truth could be beneficial in many scenarios, especially with data from unseen domains, where performance can be highly inconsistent.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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
Multi-encoder multi-resolution framework for end-to-end speech recognition
In this work, we present a novel Multi-Encoder Multi-Resolution (MEMR) framework based on the joint CTC/Attention model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Stream attention-based multi-array end-to-end speech recognition
Automatic Speech Recognition (ASR) using multiple microphone arrays has achieved great success in the far-field robustness.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree
We define a formal framework for the representation and processing of incongruent events: starting from the notion of label hierarchy, we show how partial order on labels can be deduced from such hierarchies.
