Search Results for author:
Found 19 papers, 2 papers with code
PolyVoice: Language Models for Speech to Speech Translation
For the speech synthesis part, we adopt the existing VALL-E X approach and build a unit-based audio language model.
Parameter-Efficient Conformers via Sharing Sparsely-Gated Experts for End-to-End Speech Recognition
Experimental results show that the proposed model achieves competitive performance with 1/3 of the parameters of the encoder, compared with the full-parameter model.
ADD 2022: the First Audio Deep Synthesis Detection Challenge
Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021.
Continual Learning for Fake Audio Detection
However, fine-tuning leads to performance degradation on previous data.
Half-Truth: A Partially Fake Audio Detection Dataset
Therefore, this paper develops such a dataset for half-truth audio detection (HAD).
FSR: Accelerating the Inference Process of Transducer-Based Models by Applying Fast-Skip Regularization
It takes a lot of computation and time to predict the blank tokens, but only the non-blank tokens will appear in the final output sequence.
TSNAT: Two-Step Non-Autoregressvie Transformer Models for Speech Recognition
To address these two problems, we propose a new model named the two-step non-autoregressive transformer(TSNAT), which improves the performance and accelerating the convergence of the NAR model by learning prior knowledge from a parameters-sharing AR model.
Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT
Based on this idea, we propose a non-autoregressive speech recognition model called LASO (Listen Attentively, and Spell Once).
One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition
Inspired by the success of two-pass end-to-end models, we introduce a transformer decoder and the two-stage inference method into the streaming CTC model.
Decoupling Pronunciation and Language for End-to-end Code-switching Automatic Speech Recognition
In this paper, we propose a decoupled transformer model to use monolingual paired data and unpaired text data to alleviate the problem of code-switching data shortage.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Deep imitator: Handwriting calligraphy imitation via deep attention networks
Calligraphy imitation (CI) from a handful of target handwriting samples is such a challenging task that most of the existing writing style analysis or handwriting generation methods do not exhibit satisfactory performance.
Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition
To address this problem and improve the inference speed, we propose a spike-triggered non-autoregressive transformer model for end-to-end speech recognition, which introduces a CTC module to predict the length of the target sequence and accelerate the convergence.
Listen Attentively, and Spell Once: Whole Sentence Generation via a Non-Autoregressive Architecture for Low-Latency Speech Recognition
Without beam-search, the one-pass propagation much reduces inference time cost of LASO.
Adversarial Transfer Learning for Punctuation Restoration
The other is that POS tags are provided by an external POS tagger.
Rnn-transducer with language bias for end-to-end Mandarin-English code-switching speech recognition
Recently, language identity information has been utilized to improve the performance of end-to-end code-switching (CS) speech recognition.
Synchronous Transformers for End-to-End Speech Recognition
Once a fixed-length chunk of the input sequence is processed by the encoder, the decoder begins to predict symbols immediately.
Integrating Knowledge into End-to-End Speech Recognition from External Text-Only Data
To alleviate the above two issues, we propose a unified method called LST (Learn Spelling from Teachers) to integrate knowledge into an AED model from the external text-only data and leverage the whole context in a sentence.
Self-Attention Transducers for End-to-End Speech Recognition
Furthermore, a path-aware regularization is proposed to assist SA-T to learn alignments and improve the performance.
Learn Spelling from Teachers: Transferring Knowledge from Language Models to Sequence-to-Sequence Speech Recognition
Integrating an external language model into a sequence-to-sequence speech recognition system is non-trivial.
