Analysis of a Modern Voice Morphing Approach using Gaussian Mixture Models for Laryngectomees

This paper proposes a voice morphing system for people suffering from Laryngectomy, which is the surgical removal of all or part of the larynx or the voice box, particularly performed in cases of laryngeal cancer. A primitive method of achieving voice morphing is by extracting the source's vocal coefficients and then converting them into the target speaker's vocal parameters. In this paper, we deploy Gaussian Mixture Models (GMM) for mapping the coefficients from source to destination. However, the use of the traditional/conventional GMM-based mapping approach results in the problem of over-smoothening of the converted voice. Thus, we hereby propose a unique method to perform efficient voice morphing and conversion based on GMM,which overcomes the traditional-method effects of over-smoothening. It uses a technique of glottal waveform separation and prediction of excitations and hence the result shows that not only over-smoothening is eliminated but also the transformed vocal tract parameters match with the target. Moreover, the synthesized speech thus obtained is found to be of a sufficiently high quality. Thus, voice morphing based on a unique GMM approach has been proposed and also critically evaluated based on various subjective and objective evaluation parameters. Further, an application of voice morphing for Laryngectomees which deploys this unique approach has been recommended by this paper.Comment: 6 pages, 4 figures, 4 tables; International Journal of Computer Applications Volume 49, Number 21, July 201

Simultaneous Multispeaker Segmentation for Automatic Meeting Recognition

Vocal activity detection is an important technology for both automatic speech recognition and automatic speech understanding. In meetings, participants typically vocalize for only a fraction of the recorded time, and standard vocal activity detection algorithms for close-talk microphones have shown to be ineffective. This is primarily due to the problem of crosstalk, in which a participant’s speech appears on other participants ’ microphones, making it hard to attribute detected speech to its correct speaker. We describe an automatic multichannel segmentation system for meeting recognition, which accounts for both the observed acoustics and the inferred vocal activity states of all participants using joint multi-participant models. Our experiments show that this approach almost completely eliminates the crosstalk problem. Recent improvements to the baseline reduce the development set word error rate, achieved by a state-of-theart multi-pass speech recognition system, by 62 % relative to manual segmentation. We also observe significant performance improvements on unseen data

Military applications of automatic speech recognition and future requirements

An updated summary of the state-of-the-art of automatic speech recognition and its relevance to military applications is provided. A number of potential systems for military applications are under development. These include: (1) digital narrowband communication systems; (2) automatic speech verification; (3) on-line cartographic processing unit; (4) word recognition for militarized tactical data system; and (5) voice recognition and synthesis for aircraft cockpit