In speaker recognition, it is a problem that speech f e a-ture varies depending on sentences and time diierence. This variation is mainly attributed to the variation of phonetic information and speaker information included in speech data. If these two kinds of information are separated each other, robust speaker recognition will be realized. In this study, w e propose a speaker identiica-tion a...

On-line Speaker indexing is useful for multimedia applications such as meeting or teleconference archiving and browsing. It sequentially detects the points where a speaker identity changes in a multi-speaker audio stream, and classifies each speaker segment. The main problem of on-line processing is that we can use only current and previous information in the data stream for any decisioning. To...

Objectives: The lexical-level deficits are one of the hallmark limitations observed in early school-age children with specific language impairment. These problems are the predictors of reading problems and poor performance at school. Most studies in Persian-speaker children with specific language impairment have focused on syntax and morphology and the naming errors profile of these children ar...

Speaker recognition on the 630 speaker Timit speech database, using maximum probability selection with a simple Gaussian Mixture Model (GMM) for the data distribution for each speaker, gives above 99% correct recognition. In contrast, a powerful classifier such as a Multi Layer Perceptron (MLP), trained to estimate speaker probabilities, even on a small subset of speakers often performs no bett...

In this paper, we propose a robust speaker recognition method based on position-dependent Cepstral Mean Normalization (CMN) to compensate for the channel distortion depending on the speaker position. In the training stage, the system measures the transmission characteristics according to the speaker positions from some grid points to the microphone in the room and estimates the compensation par...

This article presents a low-latency speaker diarization system (“who is speaking now?”) based on a hybrid approach that combines a traditional offline speaker diarization system (“who spoke when?”) with an online speaker identification system. The system fulfills all requirements of the diarization task, i.e. it does not need any a-priori information about the input, including no specific speak...

The focus of this work is to identify types of segments that are difficult for speaker diarization systems. The diarization outputs of five state-of-the-art systems are analyzed on short/long segments as well as segments surrounding speaker changepoints. We found that for all five systems as the duration of the segment decreased the diarization error rate (DER) increased. Also, segments immedia...

This paper studies the overlapped speech detection for improving the performance of the summed channel speaker recognition system in NIST Speaker Recognition Evaluation (SRE). The speaker recognition system includes four main modules: voice activity detection, speaker diarization, overlapped speaker detection and speaker recognition. We adopt a GMM based overlapped speaker detection system, by ...

This paper examines combining both relevance MAP and subspace speaker adaptation processes to train GMM speaker models for use in speaker verification systems with a particular focus on short utterance lengths. The subspace speaker adaptation method involves developing a speaker GMM mean supervector as the sum of a speaker-independent prior distribution and a speaker dependent offset constraine...

Speaker-independent system is desirable in many applications where speaker-specific data do not exist. However, if speakerdependent data are available, the system could be adapted to the specific speaker such that the error rate could be significantly reduced. In this paper, DARPA Resource Management task is used as the domain to investigate the performance of speaker-adaptive speech recognitio...