Search Results for author:
Found 31 papers, 5 papers with code
Improving Joint Speech-Text Representations Without Alignment
The last year has seen astonishing progress in text-prompted image generation premised on the idea of a cross-modal representation space in which the text and image domains are represented jointly.
A Comparison of Semi-Supervised Learning Techniques for Streaming ASR at Scale
Unpaired text and audio injection have emerged as dominant methods for improving ASR performance in the absence of a large labeled corpus.
Dual Learning for Large Vocabulary On-Device ASR
Dual learning is a paradigm for semi-supervised machine learning that seeks to leverage unsupervised data by solving two opposite tasks at once.
Towards Disentangled Speech Representations
In this paper, we construct a representation learning task based on joint modeling of ASR and TTS, and seek to learn a representation of audio that disentangles that part of the speech signal that is relevant to transcription from that part which is not.
Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions
This paper presents initial Speech Recognition results on "Casual Conversations" -- a publicly released 846 hour corpus designed to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of metadata, including age, gender, and skin tone.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Cascaded Multilingual Audio-Visual Learning from Videos
In this paper, we explore self-supervised audio-visual models that learn from instructional videos.
Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings
Domain-adversarial training (DAT) and multi-task learning (MTL) are two common approaches for building accent-robust ASR models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos
Multimodal self-supervised learning is getting more and more attention as it allows not only to train large networks without human supervision but also to search and retrieve data across various modalities.
Accented Speech Recognition Inspired by Human Perception
This paper explores methods that are inspired by human perception to evaluate possible performance improvements for recognition of accented speech, with a specific focus on recognizing speech with a novel accent relative to that of the training data.
Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible Inputs
To address the first challenge, we propose a novel system that can predict intents from flexible types of inputs: speech, ASR transcripts, or both.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems
Assuming we have additional text-to-intent data (without speech) available, we investigated two techniques to improve the S2I system: (1) transfer learning, in which acoustic embeddings for intent classification are tied to fine-tuned BERT text embeddings; and (2) data augmentation, in which the text-to-intent data is converted into speech-to-intent data using a multi-speaker text-to-speech system.
AVLnet: Learning Audio-Visual Language Representations from Instructional Videos
Further, we propose a tri-modal model that jointly processes raw audio, video, and text captions from videos to learn a multi-modal semantic embedding space useful for text-video retrieval.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Distributed Training of Deep Neural Network Acoustic Models for Automatic Speech Recognition
The past decade has witnessed great progress in Automatic Speech Recognition (ASR) due to advances in deep learning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improving Efficiency in Large-Scale Decentralized Distributed Training
Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchronous Parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to perform well for large-scale deep learning tasks.
Identifying Mood Episodes Using Dialogue Features from Clinical Interviews
Bipolar disorder, a severe chronic mental illness characterized by pathological mood swings from depression to mania, requires ongoing symptom severity tracking to both guide and measure treatments that are critical for maintaining long-term health.
Challenging the Boundaries of Speech Recognition: The MALACH Corpus
This paper proposes that the community place focus on the MALACH corpus to develop speech recognition systems that are more robust with respect to accents, disfluencies and emotional speech.
Acoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition
In this paper we investigate a variant of ESGD for optimization of acoustic models for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition
On commonly used public SWB-300 and SWB-2000 ASR datasets, ADPSGD can converge with a batch size 3X as large as the one used in SSGD, thus enable training at a much larger scale.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech Recognition
In automatic speech recognition (ASR), wideband (WB) and narrowband (NB) speech signals with different sampling rates typically use separate acoustic models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
English Broadcast News Speech Recognition by Humans and Machines
With recent advances in deep learning, considerable attention has been given to achieving automatic speech recognition performance close to human performance on tasks like conversational telephone speech (CTS) recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Distributed Deep Learning Strategies For Automatic Speech Recognition
We show that we can train the LSTM model using ADPSGD in 14 hours with 16 NVIDIA P100 GPUs to reach a 7. 6% WER on the Hub5- 2000 Switchboard (SWB) test set and a 13. 1% WER on the CallHome (CH) test set.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Acoustically Grounded Word Embeddings for Improved Acoustics-to-Word Speech Recognition
Direct acoustics-to-word (A2W) systems for end-to-end automatic speech recognition are simpler to train, and more efficient to decode with, than sub-word systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks
We propose a population-based Evolutionary Stochastic Gradient Descent (ESGD) framework for optimizing deep neural networks.
Building competitive direct acoustics-to-word models for English conversational speech recognition
This is because A2W models recognize words from speech without any decoder, pronunciation lexicon, or externally-trained language model, making training and decoding with such models simple.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Direct Acoustics-to-Word Models for English Conversational Speech Recognition
Our CTC word model achieves a word error rate of 13. 0%/18. 8% on the Hub5-2000 Switchboard/CallHome test sets without any LM or decoder compared with 9. 6%/16. 0% for phone-based CTC with a 4-gram LM.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
English Conversational Telephone Speech Recognition by Humans and Machines
This then raises two issues - what IS human performance, and how far down can we still drive speech recognition error rates?
Ranked #3 on
Speech Recognition
on Switchboard + Hub500
Kernel Approximation Methods for Speech Recognition
First, in order to reduce the number of random features required by kernel models, we propose a simple but effective method for feature selection.
Training variance and performance evaluation of neural networks in speech
In this work we study variance in the results of neural network training on a wide variety of configurations in automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition
We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition.
The IBM 2015 English Conversational Telephone Speech Recognition System
We describe the latest improvements to the IBM English conversational telephone speech recognition system.
Ranked #11 on
Speech Recognition
on Switchboard + Hub500
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets
The computational complexity of kernel methods has often been a major barrier for applying them to large-scale learning problems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
