Speech Processing for Digital Home Assistants: Combining signal processing with deep-learning techniques
By Reinhold Haeb-Umbach, Shinji Watanabe, Tomohiro Nakatani, Michiel Bacchiani, Björn Hoffmeister, M. Seltzer, Heiga Zen, Mehrez Souden. 2019
152 citations recorded by the source corpus.
Once a popular theme of futuristic science fiction or far-fetched technology forecasts, digital home assistants with a spoken language interface have become a ubiquitous commodity today. This success has been made possible by major advancements in signal processing and machine learning for so-called far-field speech recognition, where the commands are spoken at a distance from the sound-capturing device. The challenges encountered are quite unique and different from many other use cases of automatic speech recognition (ASR). The purpose of this article is to describe, in a way that is amenable to the nonspecialist, the key speech processing algorithms that enable reliable, fully hands-free speech interaction with digital home assistants. These technologies include multichannel acoustic echo cancellation (MAEC), microphone array processing and dereverberation techniques for signal enhancement, reliable wake-up word and end-of-interaction detection, and high-quality speech synthesis as well as sophisticated statistical models for speech and language, learned from large amounts of heterogeneous training data. In all of these fields, deep learning (DL) has played a critical role.
Subfields: Artificial Intelligence, Speech & Audio, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Speech & Audio
In Signal Processing
Citations in the corpus
- CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings (2020, neutral): “Automatic speech recognition (ASR) performance in difficult reverberant and noisy conditions has improved tremendously in the last decade [1–6].”
- Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem (2021, background): “With the popularity of speech-related intelligent devices and related applications, front-end processing has become a popular research topic [1].”
- Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges (2022, unknown): “Inspired by this, some researchers further introduce the DL-enabled method in fields of computer vision (CV) [54], [55], [56], (natural language processing) NLP [57], [58], [59] and speech processing [60], [61], [62] into end-to-end communication system as SE approaches, which pioneered the modern S”