TIMIT

About: TIMIT is a research topic. Over the lifetime, 1401 publications have been published within this topic receiving 59888 citations. The topic is also known as: TIMIT Acoustic-Phonetic Continuous Speech Corpus.

Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity

Abstract: This paper presents a new approach for unsupervised Spoken Term Detection with spoken queries using multiple sets of acoustic patterns automatically discovered from the target corpus. The different pattern HMM configurations(number of states per model, number of distinct models, number of Gaussians per state)form a three-dimensional model granularity space. Different sets of acoustic patterns automatically discovered on different points properly distributed over this three-dimensional space are complementary to one another, thus can jointly capture the characteristics of the spoken terms. By representing the spoken content and spoken query as sequences of acoustic patterns, a series of approaches for matching the pattern index sequences while considering the signal variations are developed. In this way, not only the on-line computation load can be reduced, but the signal distributions caused by different speakers and acoustic conditions can be reasonably taken care of. The results indicate that this approach significantly outperformed the unsupervised feature-based DTW baseline by 16.16\% in mean average precision on the TIMIT corpus.

Signal Bias Removal based GMM for robust speaker recognition

Abstract: In this paper, we focus on the combined method of SBR and GMM-UBM and its capacity for detection and robustness of speaker recognition. While each method has achieved improvements independent of each other in an orthogonal field, both methods have a similar framework. The proposed Signal Bias Removal based GMM (SBR-GMM) executes the minimization of the environmental variation on mismatched condition by removing the bias of the distorted input signal and the adaptation of the speaker-dependent characteristics from the clean, text independent and speaker independent background GMM. In our experiments, we compared the closed-set speaker identification for conventional CMS and the proposed method respectively on TIMIT and NTIMIT database. Particularly in the third set of experiments on NTIMIT, compared to CMS, we were able to improve the recognition rate by 27.4% using the robust feature.

Boosting speaker identification performance using a frame level based algorithm

Abstract: In this paper, we propose an algorithm to improve the performance of speaker identification systems. A baseline speaker identification system uses a scoring of a test utterance against all speakers' models; this could be termed as an evaluation at the observation level. In the proposed approach, and prior to the standard evaluation phase, an algorithm based on a frame level evaluation is applied. The speaker identification study is conducted using IVIE corpus and a randomly selected 120 speakers from TIMIT. Mel-frequency cepstral coefficients (MFCC) and Gaussian mixture model (GMM) are the main components in state of the art speaker identification systems and will be adopted in this work. Experimental results based on several systems with different training and testing conditions, showed that our proposed algorithm yielded to relative reduction in error rates of 24.4 and 37.3% over the baseline systems respectively for IVIE and TIMIT. The final performances reached measured by identification error rates are 3.4% and 5.2% for IVIE and TIMIT corpuses.