2021/09/17 by Sivanand Achanta, Achanta, Sivanand, Albert Antony +19 · 1 voice · 2 citations
Computer Science · Engineering · #Artificial intelligence #Artificial neural network #Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #Computer network #Computer science #Construct (python library) #FOS: Computer and information sciences #FOS: Electrical engineering #Natural Language Processing Techniques #PSQM #Performance (cs.PF) #Robustness (evolution) #Sound (cs.SD) #Spectrogram #Speech Recognition and Synthesis #Speech and dialogue systems #Speech processing #Speech recognition #Speech synthesis #Voice activity detection #cs.CL #cs.PF #cs.SD #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2109.08710
published in arXiv (Cornell University) (Cornell University) · 7 pages 2 figures, accepted to ASRU 2021
arxiv created 2021/09/17 · openalex publication_date 2021/09/17 · arxiv published 2021/09/17 · arxiv updated 2021/09/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Recent advances in text-to-speech (TTS) synthesis, such as Tacotron and WaveRNN, have made it possible to construct a fully neural network based TTS system, by coupling the two components together. Such a system is conceptually simple as it only takes grapheme or phoneme input, uses Mel-spectrogram as an intermediate feature, and directly generates speech samples. The system achieves quality equal or close to natural speech. However, the high computational cost of the system and issues with robustness have limited their usage in real-world speech synthesis applications and products. In this paper, we present key modeling improvements and optimization strategies that enable deploying these models, not only on GPU servers, but also on mobile devices. The proposed system can generate high-quality 24 kHz speech at 5x faster than real time on server and 3x faster than real time on mobile devices.