19 citations · 54 across the 9 of their papers we have counts for
10 papers
Attention-based Neural Beamforming Layers for Multi-channel Speech Recognition
Bhargav Pulugundla, Yang Gao, Brian King +5
Attention-based beamformers have recently been shown to be effective for multi-channel speech recognition. However, they are less capable at capturing local information. In this wo…
Wav2vec-C: A Self-supervised Model for Speech Representation Learning
Samik Sadhu, Di He, Che-Wei Huang +6
Wav2vec-C introduces a novel representation learning technique combining elements from wav2vec 2.0 and VQ-VAE. Our model learns to reproduce quantized representations from partiall…
Streaming ResLSTM with Causal Mean Aggregation for Device-Directed Utterance Detection
Xiaosu Tong, Che-Wei Huang, Sri Harish Mallidi +5
In this paper, we propose a streaming model to distinguish voice queries intended for a smart-home device from background speech. The proposed model consists of multiple CNN layers…
Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech Recognition
Maarten Van Segbroeck, Harish Mallidih, Brian King +3
Acoustic models in real-time speech recognition systems typically stack multiple unidirectional LSTM layers to process the acoustic frames over time. Performance improvements over…
Multi-Stream End-to-End Speech Recognition
Ruizhi Li, Xiaofei Wang, Sri Harish Mallidi +3
Attention-based methods and Connectionist Temporal Classification (CTC) network have been promising research directions for end-to-end (E2E) Automatic Speech Recognition (ASR). The…
Multi-encoder multi-resolution framework for end-to-end speech recognition
Ruizhi Li, Xiaofei Wang, Sri Harish Mallidi +3
Attention-based methods and Connectionist Temporal Classification (CTC) network have been promising research directions for end-to-end Automatic Speech Recognition (ASR). The joint…
Stream attention-based multi-array end-to-end speech recognition
Xiaofei Wang, Ruizhi Li, Sri Harish Mallid +3
Automatic Speech Recognition (ASR) using multiple microphone arrays has achieved great success in the far-field robustness. Taking advantage of all the information that each array…
Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling
Jaejin Cho, Murali Karthick Baskar, Ruizhi Li +6
Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively new direction in speech research. The approach benefits by performing model training without using lexi…
Device-directed Utterance Detection
Sri Harish Mallidi, Roland Maas, Kyle Goehner +3
In this work, we propose a classifier for distinguishing device-directed queries from background speech in the context of interactions with voice assistants. Applications include r…
On the Relevance of Auditory-Based Gabor Features for Deep Learning in Automatic Speech Recognition
Angel Mario Castro Martinez, Sri Harish Mallidi, Bernd T. Meyer
Previous studies support the idea of merging auditory-based Gabor features with deep learning architectures to achieve robust automatic speech recognition, however, the cause behin…