26 citations · 85 across the 16 of their papers we have counts for
5 papers · 1 filter
Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data
Alëna Aksënova, Zhehuai Chen, Chung-Cheng Chiu +8
Building inclusive speech recognition systems is a crucial step towards developing technologies that speakers of all language varieties can use. Therefore, ASR systems must work fo…
LSTM Acoustic Models Learn to Align and Pronounce with Graphemes
Arindrima Datta, Guanlong Zhao, Bhuvana Ramabhadran +1
Automated speech recognition coverage of the world's languages continues to expand. However, standard phoneme based systems require handcrafted lexicons that are difficult and expe…
Language-agnostic Multilingual Modeling
Arindrima Datta, Bhuvana Ramabhadran, Jesse Emond +2
Multilingual Automated Speech Recognition (ASR) systems allow for the joint training of data-rich and data-scarce languages in a single model. This enables data and parameter shari…
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior
Guangzhi Sun, Yu Zhang, Ron J. Weiss +5
Recent neural text-to-speech (TTS) models with fine-grained latent features enable precise control of the prosody of synthesized speech. Such models typically incorporate a fine-gr…
Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model
Anjuli Kannan, Arindrima Datta, Tara N. Sainath +6
Multilingual end-to-end (E2E) models have shown great promise in expansion of automatic speech recognition (ASR) coverage of the world's languages. They have shown improvement over…