507 citations · 761 across the 20 of their papers we have counts for
30 papers
Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers
Cheng-Ping Hsieh, Subhankar Ghosh, Boris Ginsburg
Fine-tuning is a popular method for adapting text-to-speech (TTS) models to new speakers. However this approach has some challenges. Usually fine-tuning requires several hours of h…
Damage Control During Domain Adaptation for Transducer Based Automatic Speech Recognition
Somshubra Majumdar, Shantanu Acharya, Vitaly Lavrukhin +1
Automatic speech recognition models are often adapted to improve their accuracy in a new domain. A potential drawback of model adaptation to new domains is catastrophic forgetting,…
Multi-scale Speaker Diarization with Dynamic Scale Weighting
Tae Jin Park, Nithin Rao Koluguri, Jagadeesh Balam +1
Speaker diarization systems are challenged by a trade-off between the temporal resolution and the fidelity of the speaker representation. By obtaining a superior temporal resolutio…
Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text Normalization
Evelina Bakhturina, Yang Zhang, Boris Ginsburg
Text normalization (TN) systems in production are largely rule-based using weighted finite-state transducers (WFST). However, WFST-based systems struggle with ambiguous input when…
Mixer-TTS: non-autoregressive, fast and compact text-to-speech model conditioned on language model embeddings
Oktai Tatanov, Stanislav Beliaev, Boris Ginsburg
This paper describes Mixer-TTS, a non-autoregressive model for mel-spectrogram generation. The model is based on the MLP-Mixer architecture adapted for speech synthesis. The basic…
TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context
Nithin Rao Koluguri, Taejin Park, Boris Ginsburg
In this paper, we propose TitaNet, a novel neural network architecture for extracting speaker representations. We employ 1D depth-wise separable convolutions with Squeeze-and-Excit…