Sine-wave amplitude coding using wavelet basis functions

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (leaves 106-109).by Pankaj Oberoi.M.S

Postfiltering techniques in low bit-rate speech coders

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 78-80).by Azhar K. Mustapha.M.Eng

Development of a sensory substitution API

2018 Summer.Includes bibliographical references.Sensory substitution – or the practice of mapping information from one sensory modality to another – has been shown to be a viable technique for non-invasive sensory replacement and augmentation. With the rise in popularity, ubiquity, and capability of mobile devices and wearable electronics, sensory substitution research has seen a resurgence in recent years. Due to the standard features of mobile/wearable electronics such as Bluetooth, multicore processing, and audio recording, these devices can be used to drive sensory substitution systems. Therefore, there exists a need for a flexible, extensible software package capable of performing the required real-time data processing for sensory substitution, on modern mobile devices. The primary contribution of this thesis is the development and release of an Open Source Application Programming Interface (API) capable of managing an audio stream from the source of sound to a sensory stimulus interface on the body. The API (named Tactile Waves) is written in the Java programming language and packaged as both a Java library (JAR) and Android library (AAR). The development and design of the library is presented, and its primary functions are explained. Implementation details for each primary function are discussed. Performance evaluation of all processing routines is performed to ensure real-time capability, and the results are summarized. Finally, future improvements to the library and additional applications of sensory substitution are proposed

Intelligibility enhancement of synthetic speech in noise

EC Seventh Framework Programme (FP7/2007-2013)Speech technology can facilitate human-machine interaction and create new communication interfaces. Text-To-Speech (TTS) systems provide speech output for dialogue, notification and reading applications as well as personalized voices for people that have lost the use of their own. TTS systems are built to produce synthetic voices that should sound as natural, expressive and intelligible as possible and if necessary be similar to a particular speaker. Although naturalness is an important requirement, providing the correct information in adverse conditions can be crucial to certain applications. Speech that adapts or reacts to different listening conditions can in turn be more expressive and natural. In this work we focus on enhancing the intelligibility of TTS voices in additive noise. For that we adopt the statistical parametric paradigm for TTS in the shape of a hidden Markov model (HMM-) based speech synthesis system that allows for flexible enhancement strategies. Little is known about which human speech production mechanisms actually increase intelligibility in noise and how the choice of mechanism relates to noise type, so we approached the problem from another perspective: using mathematical models for hearing speech in noise. To find which models are better at predicting intelligibility of TTS in noise we performed listening evaluations to collect subjective intelligibility scores which we then compared to the models’ predictions. In these evaluations we observed that modifications performed on the spectral envelope of speech can increase intelligibility significantly, particularly if the strength of the modification depends on the noise and its level. We used these findings to inform the decision of which of the models to use when automatically modifying the spectral envelope of the speech according to the noise. We devised two methods, both involving cepstral coefficient modifications. The first was applied during extraction while training the acoustic models and the other when generating a voice using pre-trained TTS models. The latter has the advantage of being able to address fluctuating noise. To increase intelligibility of synthetic speech at generation time we proposed a method for Mel cepstral coefficient modification based on the glimpse proportion measure, the most promising of the models of speech intelligibility that we evaluated. An extensive series of listening experiments demonstrated that this method brings significant intelligibility gains to TTS voices while not requiring additional recordings of clear or Lombard speech. To further improve intelligibility we combined our method with noise-independent enhancement approaches based on the acoustics of highly intelligible speech. This combined solution was as effective for stationary noise as for the challenging competing speaker scenario, obtaining up to 4dB of equivalent intensity gain. Finally, we proposed an extension to the speech enhancement paradigm to account for not only energetic masking of signals but also for linguistic confusability of words in sentences. We found that word level confusability, a challenging value to predict, can be used as an additional prior to increase intelligibility even for simple enhancement methods like energy reallocation between words. These findings motivate further research into solutions that can tackle the effect of energetic masking on the auditory system as well as on higher levels of processing