Search Results for author:
Found 22 papers, 9 papers with code
Listening to Multi-talker Conversations: Modular and End-to-end Perspectives
For this, we describe the Streaming Unmixing and Recognition Transducer (SURT).
On Speaker Attribution with SURT
The Streaming Unmixing and Recognition Transducer (SURT) has recently become a popular framework for continuous, streaming, multi-talker speech recognition (ASR).
Updated Corpora and Benchmarks for Long-Form Speech Recognition
The vast majority of ASR research uses corpora in which both the training and test data have been pre-segmented into utterances.
Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition
Training automatic speech recognition (ASR) systems requires large amounts of well-curated paired data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Training dynamic models using early exits for automatic speech recognition on resource-constrained devices
In self-attention models for automatic speech recognition (ASR), early-exit architectures enable the development of dynamic models capable of adapting their size and architecture to varying levels of computational resources and ASR performance demands.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios
The CHiME challenges have played a significant role in the development and evaluation of robust automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
SURT 2.0: Advances in Transducer-based Multi-talker Speech Recognition
The Streaming Unmixing and Recognition Transducer (SURT) model was proposed recently as an end-to-end approach for continuous, streaming, multi-talker speech recognition (ASR).
GPU-accelerated Guided Source Separation for Meeting Transcription
In this paper, we describe our improved implementation of GSS that leverages the power of modern GPU-based pipelines, including batched processing of frequencies and segments, to provide 300x speed-up over CPU-based inference.
Ranked #2 on
Speech Recognition
on LibriCSS
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings
Self-supervised learning (SSL) methods which learn representations of data without explicit supervision have gained popularity in speech-processing tasks, particularly for single-talker applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Anchored Speech Recognition with Neural Transducers
In this paper, we investigate anchored speech recognition to make neural transducers robust to background speech.
Low-Latency Speech Separation Guided Diarization for Telephone Conversations
In particular, we compare two low-latency speech separation models.
Injecting Text and Cross-lingual Supervision in Few-shot Learning from Self-Supervised Models
Self-supervised model pre-training has recently garnered significant interest, but relatively few efforts have explored using additional resources in fine-tuning these models.
Continuous Streaming Multi-Talker ASR with Dual-path Transducers
Streaming recognition of multi-talker conversations has so far been evaluated only for 2-speaker single-turn sessions.
Target-speaker Voice Activity Detection with Improved I-Vector Estimation for Unknown Number of Speaker
Target-speaker voice activity detection (TS-VAD) has recently shown promising results for speaker diarization on highly overlapped speech.
Reformulating DOVER-Lap Label Mapping as a Graph Partitioning Problem
We also derive an approximation bound for the algorithm in terms of the maximum number of hypotheses speakers.
The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap
This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge.
Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis
Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs
Several advances have been made recently towards handling overlapping speech for speaker diarization.
Audio and Speech Processing Sound
CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings
Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the 6th CHiME Speech Separation and Recognition Challenge (CHiME-6).
Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement
This work introduces sequential neural beamforming, which alternates between neural network based spectral separation and beamforming based spatial separation.
Probing the Information Encoded in X-vectors
Deep neural network based speaker embeddings, such as x-vectors, have been shown to perform well in text-independent speaker recognition/verification tasks.
