Search Results for author:
Found 17 papers, 3 papers with code
A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis
Unlike mainstream classifiers that wait until the last fully-connected layer to incorporate class information to make predictions, we investigate a proactive approach, asking each class to search for itself in an image.
Dual-Attention Neural Transducers for Efficient Wake Word Spotting in Speech Recognition
We present dual-attention neural biasing, an architecture designed to boost Wake Words (WW) recognition and improve inference time latency on speech recognition tasks.
Dialog act guided contextual adapter for personalized speech recognition
Specifically, it leverages dialog acts to select the most relevant user catalogs and creates queries based on both -- the audio as well as the semantic relationship between the carrier phrase and user catalogs to better guide the contextual biasing.
Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition
We augment the MC fusion networks to a conformer transducer model and train it in an end-to-end fashion.
ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognition
Very recently, as an alternative to LSTM layers, the Conformer architecture was introduced where the encoder of RNN-T is replaced with a modified Transformer encoder composed of convolutional layers at the frontend and between attention layers.
Compute Cost Amortized Transformer for Streaming ASR
We present a streaming, Transformer-based end-to-end automatic speech recognition (ASR) architecture which achieves efficient neural inference through compute cost amortization.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Contextual Adapters for Personalized Speech Recognition in Neural Transducers
Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR) models is a challenge due to the lack of training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding
In addition, the NLU model in the two-stage system is not streamable, as it must wait for the audio segments to complete processing, which ultimately impacts the latency of the SLU system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding
Recent years have seen significant advances in end-to-end (E2E) spoken language understanding (SLU) systems, which directly predict intents and slots from spoken audio.
Natural Language Understanding
Spoken Language Understanding
Context-Aware Transformer Transducer for Speech Recognition
We also leverage both BLSTM and pretrained BERT based models to encode contextual data and guide the network training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-Channel Transformer Transducer for Speech Recognition
In this paper, we present a novel speech recognition model, Multi-Channel Transformer Transducer (MCTT), which features end-to-end multi-channel training, low computation cost, and low latency so that it is suitable for streaming decoding in on-device speech recognition.
Sparsification via Compressed Sensing for Automatic Speech Recognition
In the literature, such methods are referred to as sparse pruning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-End Multi-Channel Transformer for Speech Recognition
Transformers are powerful neural architectures that allow integrating different modalities using attention mechanisms.
Pose-variant 3D Facial Attribute Generation
We address the challenging problem of generating facial attributes using a single image in an unconstrained pose.
ExpNet: Landmark-Free, Deep, 3D Facial Expressions
Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.
Ranked #1 on
3D Facial Expression Recognition
on 2017_test set
(using extra training data)
FacePoseNet: Making a Case for Landmark-Free Face Alignment
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
Ranked #1 on
Facial Landmark Detection
on 300W
(Mean Error Rate metric)
Multiple Structured-Instance Learning for Semantic Segmentation with Uncertain Training Data
By treating a bounding box as a bag with its segment hypotheses as structured instances, MSIL-CRF selects the most likely segment hypotheses by leveraging the knowledge derived from both the labeled and uncertain training data.
