Petsatodis, Theodoros

English

Voice Activity Detection (VAD), being in the focus of speech processing research formany years, is nowadays a mature technology with application in several sectors. EmbeddedVAD components in telecommunications systems (like in cellular telephony)attempt to reduce power consumption of transmitters and bandwidth utilization. VADtechnology is also integrated in speech-processing systems, such as Speaker Identification,Automatic Event Detection, and Automatic Speech Recognition, to prevent theiroperation in the absence of speech, and thus reduce the error rates of each of thesesystems.The performance of VAD systems depends strongly on various factors, including thediscriminative ability of the classification criterion employed, the dynamics of the additivenoise and the signal to noise ratio. Speech signals transmitted within reverberantenclosures and captured using far-field microphones are subject to reverberation effects,competitive sound sources, and speaker movement. Furthermore, speech distributionvaries with time and can be affected by several unpredictable factors including speaker’stemper, mood, gender, age, and more. Thus, during the design phase of a VAD, specialconsiderations have to be taken in order to build a robust system able to operate undervariable and adverse conditions.Given that for most of speech processing systems it is of crucial importance to have areasonable approximation for the probability density function (pdf) of speech, understandingthe properties of speech distribution plays a very important role in the designof speech processing systems. Within the framework of this work, variability of speechdistribution, when using far-field microphones, is analysed under the presence of noiseand reverberation.Observations of how speech distribution is shaped by external interferences are thenused as the basis to develop an adaptive unsupervised VAD scheme. This VAD, incontrary to other approaches employing fixed distribution assumptions, relies on effectivelymodelling the distribution of speech as convex combination of a Gaussian,a Laplacian, and a two-sided Gamma distribution. The increased adaptability of thesystem along with the encapsulated adaptive threshold allows the system to performremarkably under adverse complex phenomena.Following recent technological trends, of incorporating microphone arrays in numerouscommercial applications (eg. mobile phones, VOIP terminals) and research environments(smart rooms), a multiple microphone VAD is also considered. The systemprocesses signals captured by far-field sensors in order to integrate spatial informationin addition to the frequency content available at a single sensor. The core of the systemresides on the modification of a multiple observation hypothesis, testing at each sensorthe probability of having an active speaker and then fusing the decisions. The VADoperates without the need of Direction-of-Arrival (DOA) estimation and eliminates additionaldelay imposed by previous multi-microphone VAD technologies.The system developed for the multi-microphone VAD serves as the platform to mergeVAD with a very powerful analysis framework namely, the Empirical Mode Decomposition(EMD). This highly efficient method relies on local characteristics of time scale ofthe data to analyse and decompose non-stationary signals into a set of so called intrinsicmode functions (IMF). These functions are injected to the multiple microphone VADscheme in order to decide upon speech presence or absence. The outcome of this proceduredemonstrates significantly enhanced performance compared to single microphoneapproaches.Speech distribution information is also encapsulated in a supervised VAD scheme. Operatingin the far-field, the core of the system employs Hidden Markov Models thestates of which are modelled using Gaussian Mixture Models to cater for the dynamicsof captured speech. Given the bi-modality of speech production, a simple visual-VADis also developed to examine performance enhancement when fusing audio and videoinformation.In the final part of the work, applications of VAD in the context of integration with othersignal processing systems are also considered. Performance benefits of combining themulti-microphone VAD with DOA estimation are demonstrated. Optimization throughadaptation of speech shape characteristics in the embedded Time Delay Estimation(TDE) scheme is also considered, the same way that was beneficial for the convexcombination based VAD. Towards this direction, the underlying assumption of Gaussiandistributed source is replaced by that of Generalized Gaussian distribution that allowsthe evaluation of the problem under a larger set of speech-shaped distributions, rangingfrom Gaussian to Laplacian and Gamma. The analysis performed, revealed a significantresearch outcome.Furthermore, performance enhancement when using VAD in combination with noisereduction systems is also discussed in terms of residual suppression within silence intervals.For this scope, a noise reduction architecture has been developed based oncascading an one-pass scheme.The final application of VAD, examined in the thesis, is in the area of biomedicalsignal processing. A modification of one of the VAD systems developed, is employedto provide preliminary detection of one of the major breathing-related sleep disorders,apnea. The idea behind the development of this system is the capability of unobtrusivelymonitoring patients at home, improving the reliability of detection of sleep disorders inhome environments, offering comfort and time saving to patients

Far-Field Voice Activity Detection and Its Applications in Adverse Acoustic Environments

https://vbn.aau.dk/ws/files/316411633/PhD_Dissertation.pdf

Far-Field Voice Activity Detection and Its Applications in Adverse Acoustic Environments

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