"This is Houston. Say again, please". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)
August 04, 2020 Β· Declared Dead Β· π Interspeech
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Arseniy Gorin, Daniil Kulko, Steven Grima, Alex Glasman
arXiv ID
2008.01504
Category
eess.AS: Audio & Speech
Cross-listed
cs.CL,
cs.SD
Citations
3
Venue
Interspeech
Last Checked
3 months ago
Abstract
We describe the speech activity detection (SAD), speaker diarization (SD), and automatic speech recognition (ASR) experiments conducted by the Behavox team for the Interspeech 2020 Fearless Steps Challenge (FSC-2). A relatively small amount of labeled data, a large variety of speakers and channel distortions, specific lexicon and speaking style resulted in high error rates on the systems which involved this data. In addition to approximately 36 hours of annotated NASA mission recordings, the organizers provided a much larger but unlabeled 19k hour Apollo-11 corpus that we also explore for semi-supervised training of ASR acoustic and language models, observing more than 17% relative word error rate improvement compared to training on the FSC-2 data only. We also compare several SAD and SD systems to approach the most difficult tracks of the challenge (track 1 for diarization and ASR), where long 30-minute audio recordings are provided for evaluation without segmentation or speaker information. For all systems, we report substantial performance improvements compared to the FSC-2 baseline systems, and achieved a first-place ranking for SD and ASR and fourth-place for SAD in the challenge.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Audio & Speech
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
LPCNet: Improving Neural Speech Synthesis Through Linear Prediction
R.I.P.
π»
Ghosted
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
R.I.P.
π»
Ghosted
TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech
R.I.P.
π»
Ghosted
Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders
R.I.P.
π»
Ghosted
Utterance-level Aggregation For Speaker Recognition In The Wild
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted