90 citations · 224 across the 14 of their papers we have counts for
15 papers
E-Branchformer: Branchformer with Enhanced merging for speech recognition
Kwangyoun Kim, Felix Wu, Yifan Peng +4
Conformer, combining convolution and self-attention sequentially to capture both local and global information, has shown remarkable performance and is currently regarded as the sta…
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
Felix Wu, Kwangyoun Kim, Shinji Watanabe +4
We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete repres…
On the Use of External Data for Spoken Named Entity Recognition
Ankita Pasad, Felix Wu, Suwon Shon +2
Spoken language understanding (SLU) tasks involve mapping from speech audio signals to semantic labels. Given the complexity of such tasks, good performance might be expected to re…
SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech
Suwon Shon, Ankita Pasad, Felix Wu +4
Progress in speech processing has been facilitated by shared datasets and benchmarks. Historically these have focused on automatic speech recognition (ASR), speaker identification,…
SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition
Jing Pan, Tao Lei, Kwangyoun Kim +2
The Transformer architecture has been well adopted as a dominant architecture in most sequence transduction tasks including automatic speech recognition (ASR), since its attention…
Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition
Felix Wu, Kwangyoun Kim, Jing Pan +3
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture…
Multi-mode Transformer Transducer with Stochastic Future Context
Kwangyoun Kim, Felix Wu, Prashant Sridhar +2
Automatic speech recognition (ASR) models make fewer errors when more surrounding speech information is presented as context. Unfortunately, acquiring a larger future context leads…
Leveraging Pre-trained Language Model for Speech Sentiment Analysis
Suwon Shon, Pablo Brusco, Jing Pan +2
In this paper, we explore the use of pre-trained language models to learn sentiment information of written texts for speech sentiment analysis. First, we investigate how useful a p…
A Review of Speaker Diarization: Recent Advances with Deep Learning
Tae Jin Park, Naoyuki Kanda, Dimitrios Dimitriadis +3
Speaker diarization is a task to label audio or video recordings with classes that correspond to speaker identity, or in short, a task to identify "who spoke when". In the early ye…
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition
Jing Pan, Joshua Shapiro, Jeremy Wohlwend +3
In this paper we present state-of-the-art (SOTA) performance on the LibriSpeech corpus with two novel neural network architectures, a multistream CNN for acoustic modeling and a se…
Multistream CNN for Robust Acoustic Modeling
Kyu J. Han, Jing Pan, Venkata Krishna Naveen Tadala +2
This paper proposes multistream CNN, a novel neural network architecture for robust acoustic modeling in speech recognition tasks. The proposed architecture processes input speech…
Speaker Diarization with Lexical Information
Tae Jin Park, Kyu J. Han, Jing Huang +4
This work presents a novel approach for speaker diarization to leverage lexical information provided by automatic speech recognition. We propose a speaker diarization system that c…
Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap
Tae Jin Park, Kyu J. Han, Manoj Kumar +1
In this study, we propose a new spectral clustering framework that can auto-tune the parameters of the clustering algorithm in the context of speaker diarization. The proposed fram…
State-of-the-Art Speech Recognition Using Multi-Stream Self-Attention With Dilated 1D Convolutions
Kyu J. Han, Ramon Prieto, Kaixing Wu +1
Self-attention has been a huge success for many downstream tasks in NLP, which led to exploration of applying self-attention to speech problems as well. The efficacy of self-attent…
The CAPIO 2017 Conversational Speech Recognition System
Kyu J. Han, Akshay Chandrashekaran, Jungsuk Kim +1
In this paper we show how we have achieved the state-of-the-art performance on the industry-standard NIST 2000 Hub5 English evaluation set. We explore densely connected LSTMs, insp…