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1
Introduction
2
Contents
3
Conventional techniques
4
Commercialization
5
Special Recognition Systems
6
TC Loss
7
AttentionBased Approach
8
AttentionBased Problem
9
Monotonic Trunkwise Attention
10
Model Compression
11
Custom Recursion
12
Speech Request Latency
13
Hybrid Approach
14
Summary
15
NPU
16
Low end devices
17
Future applications
Description:
Explore a comprehensive review of on-device fully neural end-to-end speech recognition and synthesis algorithms in this 49-minute plenary talk from tinyML Asia 2021. Delve into the evolution from conventional speech recognition systems to modern fully neural network-based approaches. Examine various end-to-end automatic speech recognition and speech synthesis algorithms, including CTC, RNN-T, AED, MoChA, and transformer-based systems. Learn about the challenges of implementing traditional systems on devices and how neural network-based solutions offer smaller memory footprints. Discover advancements in Text-to-Speech (TTS) technology, from parametric and concatenative approaches to neural speech synthesis methods like Tacotron and Wavenet. Gain insights into model compression techniques, custom recursion, and hybrid approaches for improving speech recognition latency. Investigate the potential of NPUs and applications for low-end devices, and explore future possibilities in the field of speech technology. Read more

On-Device Neural End-to-End Speech Recognition and Synthesis Algorithms - A Review

tinyML
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