Speaker recognition

About: Speaker recognition is a research topic. Over the lifetime, 14990 publications have been published within this topic receiving 310061 citations.

Deep neural networks for single-channel multi-talker speech recognition

Abstract: We investigate techniques based on deep neural networks (DNNs) for attacking the single-channel multi-talker speech recognition problem. Our proposed approach contains five key ingredients: a multi-style training strategy on artificially mixed speech data, a separate DNN to estimate senone posterior probabilities of the louder and softer speakers at each frame, a weighted finite-state transducer (WFST)-based two-talker decoder to jointly estimate and correlate the speaker and speech, a speaker switching penalty estimated from the energy pattern change in the mixed-speech, and a confidence based system combination strategy. Experiments on the 2006 speech separation and recognition challenge task demonstrate that our proposed DNN-based system has remarkable noise robustness to the interference of a competing speaker. The best setup of our proposed systems achieves an average word error rate (WER) of 18.8% across different SNRs and outperforms the state-of-the-art IBM superhuman system by 2.8% absolute with fewer assumptions.

ALIZE/SpkDet: a state-of-the-art open source software for speaker recognition

Abstract: This paper presents the ALIZE/SpkDet open source software packages for text independent speaker recognition. This software is based on the well-known UBM/GMM approach. It includes also the latest speaker recognition developments such as Latent Factor Analysis (LFA) and unsupervised adaptation. Discriminant classifiers such as SVM supervectors are also provided , linked with the Nuisance Attribute Projection (NAP). The software performance is demonstrated within the framework of the NIST'06 SRE evaluation campaign. Several other applications like speaker diarization, embedded speaker recognition , password dependent speaker recognition and pathological voice assessment are also presented.

Automatic speech recognition for children.

Abstract: In this paper, the acoustic and linguistic characteristics of children speech are investigated in the context of automatic speech recognition. Acoustic variability is identi ed as a major hurdle in building high performance ASR applications for children. A simple speaker normalization algorithm combining frequency warping and spectral shaping introduced in [5] is shown to reduce acoustic variability and signi cantly improve recognition performance for children speakers (by 25{ 45%). Age-dependent acoustic modeling further reduces word error rate by 10%. Piece-wise linear and phoneme-dependent frequency warping algorithms are proposed for reducing acoustic mismatch between the children and adult acoustic spaces.

Robust Speaker Recognition Using Microphone Arrays

Abstract: This paper investigates the use of microphone arrays in handsfree speaker recognition systems. Hands-free operation is preferable in many potential speaker recognition applications, however obtaining acceptable performance with a single distant microphone is problematic in real noise conditions. A possible solution to this problem is the use of microphone arrays, which have the capacity to enhance a signal based purely on knowledge of its direction of arrival. The use of microphone arrays for improving the robustness of speech recognition systems has been studied in recent times, however little research has been conducted in the area of speaker recognition. This paper discusses the application of microphone arrays to speaker recognition applications, and presents an experimental evaluation of a hands-free speaker verification application in noisy conditions.