Recent Advances in Voice Signal Processing. Fundamental Technologies. Continuous Speech Recognition.

Recent Advances in Cantonese Speech Recognition

This paper describes our recent work on automatic recognition of Cantonese. Cantonese is one of the major Chinese dialects, spoken by tens of millions of people in Southern China and Hong Kong. For isolated Cantonese syllables, a neural network based recognition algorithm has been successfully developed and the most up-to-date recognition results are presented. For continuous Cantonese speech, ...

Recent Advances in EMG Pattern Recognition for Prosthetic Control

Advances in Large Vocabulary Continuous Speech Recognition

The development of robust, accurate and efficient speech recognition systems is critical to the widespread adoption of a large number of commercial applications. These include automated customer service, broadcast news transcription and indexing, voice-activated automobile accessories, large-vocabulary voice-activated cellphone dialing, and automated directory assistance. This article provides ...

Multichannel Acoustic Signal Processing for Human/Machine Interfaces - Fundamental Problems and Recent Advances

Multichannel signal processing techniques for reproduction and acquisition of audio and speech signals at the acoustic human/machine interface offer spatial selectivity and diversity as additional degrees of freedom over single-channel schemes. In this contribution, we identify the fundamental problems for acquisition and reproduction with distant sources/ listeners as signal separation problem...

Recent Advances in Continuous Speech Recognition Using the Time-Sliced Paradigm

We developed a method called Time-Slicing [1] for the analysis of the speech signal. It enables a neural network to recognize connected speech as it comes, without having to fit the input signal into a fixed time-format, nor label or segment it phoneme by phoneme. The neural network produces an immediate hypothesis of the recognized phoneme and its size is small enough to run even on a PC. To i...