We have applied two state-of-the-art speech synthesis techniques (unit selection and HMM-based synthesis) to the synthesis of emotional speech. A series of carefully designed perceptual tests to evaluate speech quality, emotion identification rates and emotional strength were used for the six emotions which we recorded – happiness, sadness, anger, surprise, fear, disgust. For the HMM-based meth...

Currently, AT&T Labs’ Natural Voices multilingual TTS system produces high-quality synthetic speech with a largescale speech corpus [1]. In the development of such systems, automatic segmentation constitutes a major component technology. The prevalent approach for automatic segmentation in speech synthesis is Hidden Markov Model (HMM) based. Even though an HMM-based approach is the most automat...

We propose an approach to reverberant speech recognition adopting deep learning in the front-end as well as back-end of a reverberant speech recognition system, and a novel method to improve the dereverberation performance of the front-end network using phone-class information. At the front-end, we adopt a deep autoencoder (DAE) for enhancing the speech feature parameters, and speech recognitio...

The SCORPIO is a small‐size mini‐teleoperator mobile service robot for booby‐trap disposal. It can be manually controlled by an operator through a portable briefcase remote control device using joystick, keyboard and buttons. In this paper, the speech interface is described. As an auxiliary function, the remote interface allows a human operator to concent...

This paper introduces the USTC speech synthesis system for Blizzard Challenge 2006. The HMM-based parametric synthesis approach was adopted for its convenience and effectiveness in building a new voice, especially for the nonnative developers. Some useful techniques were also integrated into our system, such as minimum generation error (MGE) training, phone duration modeling and linear spectral...

In this work we extend a previously proposed NMF-based technique for speech enhancement of noisy speech to exploit a Hidden Markov Model (HMM). The NMF-based technique works by finding a sparse representation of specrogram segments of noisy speech in a dictionary containing both speech and noise exemplars, and uses the activated dictionary atoms to create a time-varying filter to enhance the no...

 It is well known that the distorted speech can be considered generated from the clean speech with the additive noise and the convolutive channel as In this paper, we present our recent study on using this structured model of physical distortion for robust automatic speech recognition. Three methods are introduced for joint compensation of additive and convolutive distortions (JAC), with diffe...