Caracterización analítica de segmentos del habla mediante clustering de vectores de coeficientes cepstrales

Este proyecto se enmarca dentro de una investigación mayor cuyo objetivo es establecer si distintos fragmentos de habla ocupan distintas regiones propias e independientes del espacio de vectores de características. En caso afirmativo, podrán sustituirse los modelos GMM de los reconocedores del habla convencionales por unidades discretas y deterministas que ayuden a reducir la dimensionalidad del problema afrontado por HMM. El primer paso de esa investigación constituye el contenido de este Proyecto Fin de Carrera, cuyo cometido es proponer un algoritmo de clustering que establezca la posibilidad de realizar una estudio analítico del habla a través de coeficientes cepstrales extraídos de fragmentos en castellano. Durante su desarrollo se realizarán pruebas que determinen la existencia de conjuntos de vectores de características, de manera que se pueda establecer una relación entre ellos y los bifonemas a los que corresponden sus vectores. Con este fin se utilizará el algoritmo de clasificación no supervisada Subtractive Clustering, que permitirá evitar la dificultad que supone el desconocimiento de la forma que esos conjuntos adoptan.This project belongs to a greater research which aims to identify a correspondence between speech fragments and regions from their feature vector space. If it were to be true, the GMM models used during the conventional speech recognition scheme could be replaced by discrete and deterministic units, helping to reduce the dimensionality of the problem solved by HMM. This Final Degree Project is the first step towards that goal. It will propose a clustering algorithm that uses the cepstral coefficients to characterize speech fragments. During its development, a series of experiments will be carried out by an unsupervised classification algorithm named Subtractive Clustering, which avoids the need of specifying some kind of model for those clusters there is no knowledge about.Ingeniería en Informátic

Robust text independent closed set speaker identification systems and their evaluation

PhD ThesisThis thesis focuses upon text independent closed set speaker identi cation. The contributions relate to evaluation studies in the presence of various types of noise and handset e ects. Extensive evaluations are performed on four databases. The rst contribution is in the context of the use of the Gaussian Mixture Model-Universal Background Model (GMM-UBM) with original speech recordings from only the TIMIT database. Four main simulations for Speaker Identi cation Accuracy (SIA) are presented including di erent fusion strategies: Late fusion (score based), early fusion (feature based) and early-late fusion (combination of feature and score based), late fusion using concatenated static and dynamic features (features with temporal derivatives such as rst order derivative delta and second order derivative delta-delta features, namely acceleration features), and nally fusion of statistically independent normalized scores. The second contribution is again based on the GMM-UBM approach. Comprehensive evaluations of the e ect of Additive White Gaussian Noise (AWGN), and Non-Stationary Noise (NSN) (with and without a G.712 type handset) upon identi cation performance are undertaken. In particular, three NSN types with varying Signal to Noise Ratios (SNRs) were tested corresponding to: street tra c, a bus interior and a crowded talking environment. The performance evaluation also considered the e ect of late fusion techniques based on score fusion, namely mean, maximum, and linear weighted sum fusion. The databases employed were: TIMIT, SITW, and NIST 2008; and 120 speakers were selected from each database to yield 3,600 speech utterances. The third contribution is based on the use of the I-vector, four combinations of I-vectors with 100 and 200 dimensions were employed. Then, various fusion techniques using maximum, mean, weighted sum and cumulative fusion with the same I-vector dimension were used to improve the SIA. Similarly, both interleaving and concatenated I-vector fusion were exploited to produce 200 and 400 I-vector dimensions. The system was evaluated with four di erent databases using 120 speakers from each database. TIMIT, SITW and NIST 2008 databases were evaluated for various types of NSN namely, street-tra c NSN, bus-interior NSN and crowd talking NSN; and the G.712 type handset at 16 kHz was also applied. As recommendations from the study in terms of the GMM-UBM approach, mean fusion is found to yield overall best performance in terms of the SIA with noisy speech, whereas linear weighted sum fusion is overall best for original database recordings. However, in the I-vector approach the best SIA was obtained from the weighted sum and the concatenated fusion.Ministry of Higher Education and Scienti c Research (MoHESR), and the Iraqi Cultural Attach e, Al-Mustansiriya University, Al-Mustansiriya University College of Engineering in Iraq for supporting my PhD scholarship