Speech reconstruction using a deep partially supervised neural network.

Statistical speech reconstruction for larynx-related dysphonia has achieved good performance using Gaussian mixture models and, more recently, restricted Boltzmann machine arrays; however, deep neural network (DNN)-based systems have been hampered by the limited amount of training data available from individual voice-loss patients. The authors propose a novel DNN structure that allows a partially supervised training approach on spectral features from smaller data sets, yielding very good results compared with the current state-of-the-art.

A comprehensive vowel space for whispered speech.

Whispered speech is a relatively common form of communications, used primarily to selectively exclude or include potential listeners from hearing a spoken message. Despite the everyday nature of whispering, and its undoubted usefulness in vocal communications, whispers have received relatively little research effort to date, apart from some studies analyzing the main whispered vowels and some quite general estimations of whispered speech characteristics. In particular, a classic vowel space determination has been lacking for whispers.

Reconstruction of normal sounding speech for laryngectomy patients through a modified CELP codec.

Whispered speech can be useful for quiet and private communication, and is the primary means of unaided spoken communication for many people experiencing voice-box deficiencies. Patients who have undergone partial or full laryngectomy are typically unable to speak anything more than hoarse whispers, without the aid of prostheses or specialized speaking techniques. Each of the current prostheses and rehabilitative methods for post-laryngectomized patients (primarily oesophageal speech, tracheo-esophageal puncture, and electrolarynx) have particular disadvantages, prompting new work on nonsurgical, noninvasive alternative solutions.