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Found 14 papers, 2 papers with code
Acoustic modeling for Overlapping Speech Recognition: JHU Chime-5 Challenge System
This paper summarizes our acoustic modeling efforts in the Johns Hopkins University speech recognition system for the CHiME-5 challenge to recognize highly-overlapped dinner party speech recorded by multiple microphone arrays.
Less Peaky and More Accurate CTC Forced Alignment by Label Priors
Connectionist temporal classification (CTC) models are known to have peaky output distributions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Self-Supervised Representations for Singing Voice Conversion
A singing voice conversion model converts a song in the voice of an arbitrary source singer to the voice of a target singer.
Voice-preserving Zero-shot Multiple Accent Conversion
Most people who have tried to learn a foreign language would have experienced difficulties understanding or speaking with a native speaker's accent.
Towards zero-shot Text-based voice editing using acoustic context conditioning, utterance embeddings, and reference encoders
Text-based voice editing (TBVE) uses synthetic output from text-to-speech (TTS) systems to replace words in an original recording.
Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings
Domain-adversarial training (DAT) and multi-task learning (MTL) are two common approaches for building accent-robust ASR models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models
Hybrid automatic speech recognition (ASR) models are typically sequentially trained with CTC or LF-MMI criteria.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition
In this paper, we introduce the Kaizen framework that uses a continuously improving teacher to generate pseudo-labels for semi-supervised speech recognition (ASR).
Large scale weakly and semi-supervised learning for low-resource video ASR
Many semi- and weakly-supervised approaches have been investigated for overcoming the labeling cost of building high quality speech recognition systems.
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).
Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages
Automatic speech recognition (ASR) systems often need to be developed for extremely low-resource languages to serve end-uses such as audio content categorization and search.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Acoustic data-driven lexicon learning based on a greedy pronunciation selection framework
Speech recognition systems for irregularly-spelled languages like English normally require hand-written pronunciations.
Using of heterogeneous corpora for training of an ASR system
The paper summarizes the development of the LVCSR system built as a part of the Pashto speech-translation system at the SCALE (Summer Camp for Applied Language Exploration) 2015 workshop on "Speech-to-text-translation for low-resource languages".
Purely sequence-trained neural networks for ASR based on lattice-free MMI
Models trained with LFMMI provide a relative word error rate reduction of ∼11. 5%, over those trained with cross-entropy objective function, and ∼8%, over those trained with cross-entropy and sMBR objective functions.
Ranked #5 on
Speech Recognition
on WSJ eval92
