Search Results for author:
Found 13 papers, 6 papers with code
E-Branchformer: Branchformer with Enhanced merging for speech recognition
Conformer, combining convolution and self-attention sequentially to capture both local and global information, has shown remarkable performance and is currently regarded as the state-of-the-art for automatic speech recognition (ASR).
Ranked #9 on
Speech Recognition
on LibriSpeech test-other
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap
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.
Ranked #1 on
Speaker Diarization
on CALLHOME
(DER(ig olp) metric)
SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech
Historically these have focused on automatic speech recognition (ASR), speaker identification, or other lower-level tasks.
Ranked #1 on
Named Entity Recognition (NER)
on SLUE
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
State-of-the-Art Speech Recognition Using Multi-Stream Self-Attention With Dilated 1D Convolutions
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.
Ranked #24 on
Speech Recognition
on LibriSpeech test-clean
On the Use of External Data for Spoken Named Entity Recognition
In this work we focus on low-resource spoken named entity recognition (NER) and address the question: Beyond self-supervised pre-training, how can we use external speech and/or text data that are not annotated for the task?
The CAPIO 2017 Conversational Speech Recognition System
This method was applied with the CallHome training corpus and improved individual system performances by on average 6. 1% (relative) against the CallHome portion of the evaluation set with no performance loss on the Switchboard portion.
Speaker Diarization with Lexical Information
This work presents a novel approach for speaker diarization to leverage lexical information provided by automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Multistream CNN for Robust Acoustic Modeling
When combined with self-attentive SRU LM rescoring, multistream CNN contributes for ASAPP to achieve the best WER of 1. 75% on test-clean in LibriSpeech.
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition
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 self-attentive simple recurrent unit (SRU) for language modeling.
Ranked #7 on
Speech Recognition
on LibriSpeech test-clean
A Review of Speaker Diarization: Recent Advances with Deep Learning
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".
Leveraging Pre-trained Language Model for Speech Sentiment Analysis
In this paper, we explore the use of pre-trained language models to learn sentiment information of written texts for speech sentiment analysis.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Multi-mode Transformer Transducer with Stochastic Future Context
A Multi-mode ASR model can fulfill various latency requirements during inference -- when a larger latency becomes acceptable, the model can process longer future context to achieve higher accuracy and when a latency budget is not flexible, the model can be less dependent on future context but still achieve reliable accuracy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
PEAVS: Perceptual Evaluation of Audio-Visual Synchrony Grounded in Viewers' Opinion Scores
Recent advancements in audio-visual generative modeling have been propelled by progress in deep learning and the availability of data-rich benchmarks.
