This paper describes a synthesis from analysis scheme for producing natural sounding intonation for speech synthesis. The paper presents a new method of describing F0 contours in terms of three basic phonetic intonation elements. Details are given of an automatic system for labelling F0 contours, which could be used for speech recognition purposes. Current work on extracting a phonological desc...

Recently, in the language testing field, automatic speech recognition (ASR) technology has been used to automatically score speaking tests. This paper investigates the impact of audio quality on ASR-based automatic speaking assessment. Using the read speech data in the International English Speaking Test (IEST) practice test, we annotated audio quality and compared scores rated by humans, speec...

This introduction to a special session on “Emotion in recognition and synthesis” highlights the need to understand the effects of affective speaker states on voice and speech on a psychophysiological level. It is argued that major advances in speaker verification, speech recognition, and natural-sounding speech synthesis depend on increases in our knowledge of the mechanisms underlying voice an...

This paper presents our novel method to encode word confusion networks, which can represent a rich hypothesis space of automatic speech recognition systems, via recurrent neural networks. We demonstrate the utility of our approach for the task of dialog state tracking in spoken dialog systems that relies on automatic speech recognition output. Encoding confusion networks outperforms encoding th...

In automatic speech recognition mel-frequency cepstral coefficients (MFCC) or linear predictive cepstral coefficients (LPCC) are features commonly used today. However, their calculation considers only a few features of the auditory system. On the assumption that the human representation of speech is an optimal representation, considering more features of the auditory system might lead to a bett...