18 citations · 41 across the 15 of their papers we have counts for
17 papers
Towards Selection of Text-to-speech Data to Augment ASR Training
Shuo Liu, Leda Sarı, Chunyang Wu +4
This paper presents a method for selecting appropriate synthetic speech samples from a given large text-to-speech (TTS) dataset as supplementary training data for an automatic spee…
Text Generation with Speech Synthesis for ASR Data Augmentation
Zhuangqun Huang, Gil Keren, Ziran Jiang +11
Aiming at reducing the reliance on expensive human annotations, data synthesis for Automatic Speech Recognition (ASR) has remained an active area of research. While prior work main…
A Token-Wise Beam Search Algorithm for RNN-T
Gil Keren
Standard Recurrent Neural Network Transducers (RNN-T) decoding algorithms for speech recognition are iterating over the time axis, such that one time step is decoded before moving…
Improving Fast-slow Encoder based Transducer with Streaming Deliberation
Ke Li, Jay Mahadeokar, Jinxi Guo +5
This paper introduces a fast-slow encoder based transducer with streaming deliberation for end-to-end automatic speech recognition. We aim to improve the recognition accuracy of th…
Scaling ASR Improves Zero and Few Shot Learning
Alex Xiao, Weiyi Zheng, Gil Keren +6
With 4.5 million hours of English speech from 10 different sources across 120 countries and models of up to 10 billion parameters, we explore the frontiers of scale for automatic s…
Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion
Duc Le, Mahaveer Jain, Gil Keren +9
How to leverage dynamic contextual information in end-to-end speech recognition has remained an active research area. Previous solutions to this problem were either designed for sp…
Deep Shallow Fusion for RNN-T Personalization
Duc Le, Gil Keren, Julian Chan +3
End-to-end models in general, and Recurrent Neural Network Transducer (RNN-T) in particular, have gained significant traction in the automatic speech recognition community in the l…
Alignment Restricted Streaming Recurrent Neural Network Transducer
Jay Mahadeokar, Yuan Shangguan, Duc Le +6
There is a growing interest in the speech community in developing Recurrent Neural Network Transducer (RNN-T) models for automatic speech recognition (ASR) applications. RNN-T is t…
Contextual RNN-T For Open Domain ASR
Mahaveer Jain, Gil Keren, Jay Mahadeokar +3
End-to-end (E2E) systems for automatic speech recognition (ASR), such as RNN Transducer (RNN-T) and Listen-Attend-Spell (LAS) blend the individual components of a traditional hybri…
N-HANS: Introducing the Augsburg Neuro-Holistic Audio-eNhancement System
Shuo Liu, Gil Keren, Björn Schuller
N-HANS is a Python toolkit for in-the-wild audio enhancement, including speech, music, and general audio denoising, separation, and selective noise or source suppression. The funct…
Single-Channel Speech Separation with Auxiliary Speaker Embeddings
Shuo Liu, Gil Keren, Björn Schuller
We present a novel source separation model to decompose asingle-channel speech signal into two speech segments belonging to two different speakers. The proposed model is a neural n…
Scaling Speech Enhancement in Unseen Environments with Noise Embeddings
Gil Keren, Jing Han, Björn Schuller
We address the problem of speech enhancement generalisation to unseen environments by performing two manipulations. First, we embed an additional recording from the environment alo…
Calibrated Prediction Intervals for Neural Network Regressors
Gil Keren, Nicholas Cummins, Björn Schuller
Ongoing developments in neural network models are continually advancing the state of the art in terms of system accuracy. However, the predicted labels should not be regarded as th…
Weakly Supervised One-Shot Detection with Attention Similarity Networks
Gil Keren, Maximilian Schmitt, Thomas Kehrenberg +1
Neural network models that are not conditioned on class identities were shown to facilitate knowledge transfer between classes and to be well-suited for one-shot learning tasks. Fo…
Fast Single-Class Classification and the Principle of Logit Separation
Gil Keren, Sivan Sabato, Björn Schuller
We consider neural network training, in applications in which there are many possible classes, but at test-time, the task is a binary classification task of determining whether the…
Tunable Sensitivity to Large Errors in Neural Network Training
Gil Keren, Sivan Sabato, Björn Schuller
When humans learn a new concept, they might ignore examples that they cannot make sense of at first, and only later focus on such examples, when they are more useful for learning.…
Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data
Gil Keren, Björn Schuller
Traditional convolutional layers extract features from patches of data by applying a non-linearity on an affine function of the input. We propose a model that enhances this feature…