Search Results for author:
Found 7 papers, 1 papers with code
OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identification
Inspired by the Open Whisper-style Speech Model (OWSM) project, we propose OWSM-CTC, a novel encoder-only speech foundation model based on Connectionist Temporal Classification (CTC).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
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.
Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search
The proposed method can be trained effectively by combining a bias phrase index loss and special tokens to detect the bias phrases in the input speech data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
4D ASR: Joint modeling of CTC, Attention, Transducer, and Mask-Predict decoders
The network architecture of end-to-end (E2E) automatic speech recognition (ASR) can be classified into several models, including connectionist temporal classification (CTC), recurrent neural network transducer (RNN-T), attention mechanism, and non-autoregressive mask-predict models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Metric-based multimodal meta-learning for human movement identification via footstep recognition
We describe a novel metric-based learning approach that introduces a multimodal framework and uses deep audio and geophone encoders in siamese configuration to design an adaptable and lightweight supervised model.
Benford's laws tests on S&P500 daily closing values and the corresponding daily log-returns both point to huge non-conformity
The so-called Benford's laws are of frequent use in order to observe anomalies and regularities in data sets, in particular, in election results and financial statements.
