Speaker recognition

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

VTLN-based voice conversion

Abstract: In speech recognition, vocal tract length normalization (VTLN) is a well-studied technique for speaker normalization. As voice conversion aims at the transformation of a source speaker's voice into that of a target speaker, we want to investigate whether VTLN is an appropriate method to adapt the voice characteristics. After applying several conventional VTLN warping functions, we extend the piecewise linear function to several segments, allowing a more detailed warping of the source spectrum. Experiments on voice conversion are performed on three corpora of two languages and both speaker genders.

Session-independent emg-based speech recognition

Abstract: This paper reports on our recent research in speech recognition by surface electromyography (EMG), which is the technology of recording the electric activation potentials of the human articulatory muscles by surface electrodes in order to recognize speech. This method can be used to create Silent Speech Interfaces, since the EMG signal is available even when no audible signal is transmitted or captured. Several past studies have shown that EMG signals may vary greatly between different recording sessions, even of one and the same speaker. This paper shows that session-independent training methods may be used to obtain robust EMGbased speech recognizers which cope well with unseen recording sessions as well as with speaking mode variations. Our best session-independent recognition system, trained on 280 utterances of 7 different sessions, achieves an average 21.93% Word Error Rate (WER) on a testing vocabulary of 108 words. The overall best session-adaptive recognition system, based on a session-independent system and adapted towards the test session with 40 adaptation sentences, achieves an average WER of 15.66%, which is a relative improvement of 21% compared to the baseline average WER of 19.96% of a session-dependent recognition system trained only on a single session of 40 sentences.

Age Estimation from Telephone Speech using i-vectors

Abstract: Motivated by the success of i-vectors in the field of speaker recognition, this paper proposes a new approach for age estimation from telephone speech patterns based on i-vectors. In this method, each utterance is modeled by its corresponding ivector. Then, Support Vector Regression (SVR) is applied to estimate the age of speakers. The proposed method is trained and tested on telephone conversations of the National Institute for Standard in Technology (NIST) 2010 and 2008 Speaker Recognition Evaluations databases. Evaluation results show that the proposed method outperforms different conventional methods in speaker age estimation.

Using jitter and shimmer in speaker verification

Abstract: Jitter and shimmer are measures of the fundamental frequency and amplitude cycle-to-cycle variations, respectively. Both features have been largely used for the description of pathological voices, and since they characterise some aspects concerning particular voices, they are expected to have a certain degree of speaker specificity. In the current work, jitter and shimmer are successfully used in a speaker verification experiment. Moreover, both measures are combined with spectral and prosodic features using several types of normalisation and fusion techniques in order to obtain better verification results. The overall speaker verification system is also improved by using histogram equalisation as a normalisation technique previous to fusing the features by support vector machines.