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Found 16 papers, 1 papers with code
Retrieve and Copy: Scaling ASR Personalization to Large Catalogs
Personalization of automatic speech recognition (ASR) models is a widely studied topic because of its many practical applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Generalized zero-shot audio-to-intent classification
Our multimodal training approach improves the accuracy of zero-shot intent classification on unseen intents of SLURP by 2. 75% and 18. 2% for the SLURP and internal goal-oriented dialog datasets, respectively, compared to audio-only training.
DCTX-Conformer: Dynamic context carry-over for low latency unified streaming and non-streaming Conformer ASR
To address this issue, we propose the integration of a novel dynamic contextual carry-over mechanism in a state-of-the-art (SOTA) unified ASR system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Dynamic Chunk Convolution for Unified Streaming and Non-Streaming Conformer ASR
Overall, our proposed model reduces the degradation of the streaming mode over the non-streaming full-contextual model from 41. 7% and 45. 7% to 16. 7% and 26. 2% on the LibriSpeech test-clean and test-other datasets respectively, while improving by a relative 15. 5% WER over the previous state-of-the-art unified model.
Device Directedness with Contextual Cues for Spoken Dialog Systems
In this work, we define barge-in verification as a supervised learning task where audio-only information is used to classify user spoken dialogue into true and false barge-ins.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards Personalization of CTC Speech Recognition Models with Contextual Adapters and Adaptive Boosting
End-to-end speech recognition models trained using joint Connectionist Temporal Classification (CTC)-Attention loss have gained popularity recently.
Adapting Long Context NLM for ASR Rescoring in Conversational Agents
Neural Language Models (NLM), when trained and evaluated with context spanning multiple utterances, have been shown to consistently outperform both conventional n-gram language models and NLMs that use limited context.
Transformer-Transducers for Code-Switched Speech Recognition
We live in a world where 60% of the population can speak two or more languages fluently.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multimodal Semi-supervised Learning Framework for Punctuation Prediction in Conversational Speech
Experiments conducted on the Fisher corpus show that our proposed approach achieves ~6-9% and ~3-4% absolute improvement (F1 score) over the baseline BLSTM model on reference transcripts and ASR outputs respectively.
Robust Prediction of Punctuation and Truecasing for Medical ASR
We also present techniques for domain and task specific adaptation by fine-tuning masked language models with medical domain data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques.
Fine-grained robust prosody transfer for single-speaker neural text-to-speech
However, when trained on a single-speaker dataset, the conventional prosody transfer systems are not robust enough to speaker variability, especially in the case of a reference signal coming from an unseen speaker.
In Other News: A Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data
Neural text-to-speech synthesis (NTTS) models have shown significant progress in generating high-quality speech, however they require a large quantity of training data.
Effect of data reduction on sequence-to-sequence neural TTS
Recent speech synthesis systems based on sampling from autoregressive neural networks models can generate speech almost undistinguishable from human recordings.
Median-Based Generation of Synthetic Speech Durations using a Non-Parametric Approach
This paper proposes a new approach to duration modelling for statistical parametric speech synthesis in which a recurrent statistical model is trained to output a phone transition probability at each timestep (acoustic frame).
DNN-based Speech Synthesis for Indian Languages from ASCII text
These methods first convert the ASCII text to a phonetic script, and then learn a Deep Neural Network to synthesize speech from that.
