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Found 13 papers, 4 papers with code
Miipher-2: A Universal Speech Restoration Model for Million-Hour Scale Data Restoration
Training data cleaning is a new application for generative model-based speech restoration (SR).
FLEURS-R: A Restored Multilingual Speech Corpus for Generation Tasks
This paper introduces FLEURS-R, a speech restoration applied version of the Few-shot Learning Evaluation of Universal Representations of Speech (FLEURS) corpus.
Lenient Evaluation of Japanese Speech Recognition: Modeling Naturally Occurring Spelling Inconsistency
Word error rate (WER) and character error rate (CER) are standard metrics in Speech Recognition (ASR), but one problem has always been alternative spellings: If one's system transcribes adviser whereas the ground truth has advisor, this will count as an error even though the two spellings really represent the same word.
LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus
The constituent samples of LibriTTS-R are identical to those of LibriTTS, with only the sound quality improved.
Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations
Experiments show that Miipher (i) is robust against various audio degradation and (ii) enable us to train a high-quality text-to-speech (TTS) model from restored speech samples collected from the Web.
Knowledge Transfer from Large-scale Pretrained Language Models to End-to-end Speech Recognizers
Since embedding vectors can be assumed as implicit representations of linguistic information such as part-of-speech, intent, and so on, those are also expected to be useful modeling cues for ASR decoders.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
SNRi Target Training for Joint Speech Enhancement and Recognition
Furthermore, by analyzing the predicted target SNRi, we observed the jointly trained network automatically controls the target SNRi according to noise characteristics.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement
To make the model computationally feasible, we extend the Conformer using linear complexity attention and stacked 1-D dilated depthwise convolution layers.
A Comparative Study on Neural Architectures and Training Methods for Japanese Speech Recognition
End-to-end (E2E) modeling is advantageous for automatic speech recognition (ASR) especially for Japanese since word-based tokenization of Japanese is not trivial, and E2E modeling is able to model character sequences directly.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Unsupervised Learning of Disentangled Speech Content and Style Representation
We present an approach for unsupervised learning of speech representation disentangling contents and styles.
ESPnet-ST: All-in-One Speech Translation Toolkit
We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework.
A Comparative Study on Transformer vs RNN in Speech Applications
Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS).
Ranked #17 on
Speech Recognition
on AISHELL-1
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
ESPnet: End-to-End Speech Processing Toolkit
This paper introduces a new open source platform for end-to-end speech processing named ESPnet.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
