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Found 10 papers, 0 papers with code
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.
Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation
The availability of data in expressive styles across languages is limited, and recording sessions are costly and time consuming.
Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module
It uses voice conversion (VC) as a post-processing module appended to a pre-existing high-quality TTS system and marks a conceptual shift in the existing TTS paradigm, framing the few-shot TTS problem as a VC task.
Cross-speaker style transfer for text-to-speech using data augmentation
The proposed approach relies on voice conversion to first generate high-quality data from the set of supporting expressive speakers.
Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech
In this paper, we present a method for building highly expressive TTS voices with as little as 15 minutes of speech data from the target speaker.
EmoCat: Language-agnostic Emotional Voice Conversion
In this work we propose EmoCat, a language-agnostic emotional voice conversion model.
Low-resource expressive text-to-speech using data augmentation
While recent neural text-to-speech (TTS) systems perform remarkably well, they typically require a substantial amount of recordings from the target speaker reading in the desired speaking style.
Voice Conversion for Whispered Speech Synthesis
We present an approach to synthesize whisper by applying a handcrafted signal processing recipe and Voice Conversion (VC) techniques to convert normally phonated speech to whispered speech.
Traditional Machine Learning for Pitch Detection
In this paper, we consider voicing detection as a classification problem and F0 contour estimation as a regression problem.
