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Found 13 papers, 6 papers with code
OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer
In this work, we aim to improve the performance and efficiency of OWSM without extra training data.
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study
Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling.
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
Pre-training speech models on large volumes of data has achieved remarkable success.
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data
To address this issue, we introduce an automatic in-the-wild speech data preprocessing framework (AutoPrep) in this paper, which is designed to enhance speech quality, generate speaker labels, and produce transcriptions automatically.
Bayes Risk Transducer: Transducer with Controllable Alignment Prediction
While the vanilla transducer does not have a prior preference for any of the valid paths, this work intends to enforce the preferred paths and achieve controllable alignment prediction.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Bayes risk CTC: Controllable CTC alignment in Sequence-to-Sequence tasks
Besides predicting the target sequence, a side product of CTC is to predict the alignment, which is the most probable input-long sequence that specifies a hard aligning relationship between the input and target units.
LAE: Language-Aware Encoder for Monolingual and Multilingual ASR
Experiments conducted on Mandarin-English code-switched speech suggest that the proposed LAE is capable of discriminating different languages in frame-level and shows superior performance on both monolingual and multilingual ASR tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Speaker-Aware Mixture of Mixtures Training for Weakly Supervised Speaker Extraction
Dominant researches adopt supervised training for speaker extraction, while the scarcity of ideally clean corpus and channel mismatch problem are rarely considered.
Integrating Lattice-Free MMI into End-to-End Speech Recognition
However, the effectiveness and efficiency of the MBR-based methods are compromised: the MBR criterion is only used in system training, which creates a mismatch between training and decoding; the on-the-fly decoding process in MBR-based methods results in the need for pre-trained models and slow training speeds.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improving Mandarin End-to-End Speech Recognition with Word N-gram Language Model
Then, the LM score of the hypothesis is obtained by intersecting the generated lattice with an external word N-gram LM.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMI
Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Layer Reduction: Accelerating Conformer-Based Self-Supervised Model via Layer Consistency
Transformer-based self-supervised models are trained as feature extractors and have empowered many downstream speech tasks to achieve state-of-the-art performance.
A Random Gossip BMUF Process for Neural Language Modeling
One important challenge of training an NNLM is to leverage between scaling the learning process and handling big data.
