Estimation of Severity of Speech Disability through Speech Envelope

In this paper, envelope detection of speech is discussed to distinguish the pathological cases of speech disabled children. The speech signal samples of children of age between five to eight years are considered for the present study. These speech signals are digitized and are used to determine the speech envelope. The envelope is subjected to ratio mean analysis to estimate the disability. This analysis is conducted on ten speech signal samples which are related to both place of articulation and manner of articulation. Overall speech disability of a pathological subject is estimated based on the results of above analysis.Comment: 8 pages,4 Figures,Signal & Image Processing Journal AIRC

The voice activity detection (VAD) recorder and VAD network recorder : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University

The project is to provide a feasibility study for the AudioGraph tool, focusing on two application areas: the VAD (voice activity detector) recorder and the VAD network recorder. The first one achieves a low bit-rate speech recording on the fly, using a GSM compression coder with a simple VAD algorithm; and the second one provides two-way speech over IP, fulfilling echo cancellation with a simplex channel. The latter is required for implementing a synchronous AudioGraph. In the first chapter we introduce the background of this project, specifically, the VoIP technology, the AudioGraph tool, and the VAD algorithms. We also discuss the problems set for this project. The second chapter presents all the relevant techniques in detail, including sound representation, speech-coding schemes, sound file formats, PowerPlant and Macintosh programming issues, and the simple VAD algorithm we have developed. The third chapter discusses the implementation issues, including the systems' objective, architecture, the problems encountered and solutions used. The fourth chapter illustrates the results of the two applications. The user documentations for the applications are given, and after that, we analyse the parameters based on the results. We also present the default settings of the parameters, which could be used in the AudioGraph system. The last chapter provides conclusions and future work

Text-Independent Voice Conversion

This thesis deals with text-independent solutions for voice conversion. It first introduces the use of vocal tract length normalization (VTLN) for voice conversion. The presented variants of VTLN allow for easily changing speaker characteristics by means of a few trainable parameters. Furthermore, it is shown how VTLN can be expressed in time domain strongly reducing the computational costs while keeping a high speech quality. The second text-independent voice conversion paradigm is residual prediction. In particular, two proposed techniques, residual smoothing and the application of unit selection, result in essential improvement of both speech quality and voice similarity. In order to apply the well-studied linear transformation paradigm to text-independent voice conversion, two text-independent speech alignment techniques are introduced. One is based on automatic segmentation and mapping of artificial phonetic classes and the other is a completely data-driven approach with unit selection. The latter achieves a performance very similar to the conventional text-dependent approach in terms of speech quality and similarity. It is also successfully applied to cross-language voice conversion. The investigations of this thesis are based on several corpora of three different languages, i.e., English, Spanish, and German. Results are also presented from the multilingual voice conversion evaluation in the framework of the international speech-to-speech translation project TC-Star