Reverberation and noise are known to be the two most important culprits for poor performance in far-field speech applications, such as automatic speech recognition. Recent research has suggested that reverberation-aware speech enhancement (or speech technologies, in general) could be used to improve performance. However, recent results also show existing blind room acoustics characterization algorithms are not robust under ambient noise and there is still room for improvement under such settings. In this paper, several fusion approaches are proposed for noise-robust reverberation time estimation. More specifically, feature- and score-level fusion of short- and long-term speech temporal dynamics features are proposed. With noise-aware feature-level fusion, gains of up to 15.4% could be seen in root mean square error. Score-level fusion, in turn, showed further improvements of up to 9.8%. Relative to a recently-proposed noise-robust benchmark algorithm, improvements of 30% could be seen, thus showing the advantages of speech temporal dynamics fusion approaches for noise-robust reverberation time estimation.

In this paper, automatic speaker verification using normal and whispered speech is explored. Typically, for speaker verification systems with varying vocal effort inputs, standard solutions such as feature mapping or addition of data during parameter estimation (training) and enrollment stages result in a trade-off between accuracy gains with whispered test data and accuracy losses (up to 70% in equal error rate, EER) with normal test data. To overcome this shortcoming, this paper proposes two innovations. First, we show the complementarity of features derived from AM-FM models over conventional mel-frequency cepstral coefficients, thus signalling the importance of instantaneous phase information for whispered speech speaker verification. Next, two fusion schemes are explored: score- and feature-level fusion. Overall, we show that gains as high as 30% and 84% in EER can be achieved for normal and whispered speech, respectively, using featurelevel fusion.

Speaker clustering is a crucial step for speaker diarization. The short duration of speech segments in telephone speech dialogue and the absence of prior information on the number of clusters dramatically increase the difficulty of this problem in diarizing spontaneous telephone speech conversations. We propose a simple iterative Mean Shift algorithm based on the cosine distance to perform speaker clustering under these conditions. Two variants of the cosine distance Mean Shift are compared in an exhaustive practical study. We report state of the art results as measured by the Diarization Error Rate and the Number of Detected Speakers on the LDC CallHome telephone corpus.

Speaker clustering is an important task in many applications such as Speaker Diarization as well as Speech Recognition. Speaker clustering can be done within a single multi-speaker recording (Diarization) or for a set of different recordings. In this work we are interested by the former case and we propose a simple iterative Mean Shift (MS) algorithm to deal with this problem. Traditionally, MS algorithm is based on Euclidean distance. We propose to use the Cosine distance in order to build a new version of MS algorithm. We report results as measured by speaker and cluster impurities on NIST SRE 2008 datasets.

This paper is a natural extension of our previous work on gender-independent speaker verification systems [1]. In a previous paper, we presented a solution to avoid using gender information in the Probabilistic Linear Discriminant Analysis (PLDA) without any loss of accuracy compared with a genderdependent base-line implementation. In this work, we propose two solutions to make a speaker verification system based on Cosine similarity independent of speaker gender. Our choice of the Cosine similarity is motivated by the fact that it is proved itself as a second state-of-the art- in parallel with PLDA- of i-vector based speaker verification systems. As measured by Equal Error Rate and min DCF’s, performance results on the extended telephone list coreext-coreext condition of SRE2010 1 show no performance decrease in gender-independent Cosine similarity system compared to gender-dependent one. Tests were also successful for genderindependent propositions on a cross gender list as done in [1].

A novel approach to supervised dimensionality reduction is introduced, based on Gaussian Restricted Boltzmann Machines. The proposed model should be considered as the analogue of the probabilistic LDA, using undirected graphical models. The training algorithm of the model is presented while its close relation to the cosine distance is underlined. For the problem of speaker verification, we applied it to i-vectors and attained a significant improvement compared to the Fisher’s Discriminant LDA projection using less than half of the number of eigenvectors required by LDA.

The work presented in this paper is an extension of our two previous works. In the first paper, we proposed a low dimensional feature (i-vectors) extractor which is suit able for both telephone and microphone data of the NIST speaker recognition evaluation dataset. The second paper introduces the use of Probabilistic Linear Discriminant Analysis (PLDA) framework with a heavy tailed distribution for speaker verification. The advantage of PLDA comes from the fact that it does not require eigenchannel modelization nor scores normalization. However, this approach is only known for its success on telephone data speech but not for micro phone data. We propose to overcome this drawback by using PLDA as a second pass at the front-end feature extraction as well as a classifier. We present results on female speakers for the interview-interview condition in NIST2010 SRE. As measured by equal error rate (ERR) and NIST detection cost function (DCF), results with raw scores are 17% better than with score normalization. We have also calibrated our scores and we achieve a minimum and an actual DCF respectively of 0.559 and 0.607.

The Speaker Recognition community that participates in NIST evaluations has concentrated on designing genderand channel-conditioned systems. In the real word, this conditioning is not feasible. Our main purpose in this work is to propose a mixture of Probabilistic Linear Discriminant Analysis models (PLDA) as a solution for making systems independent of speaker gender. In order to show the effectiveness of the mixture model, we first experiment on 2010 NIST telephone speech (det5), where we prove that there is no loss of accuracy compared with a baseline gender-dependent model. We also test with success the mixture model on a more realistic situation where there are cross-gender trials. Furthermore, we report results on microphone speech for the det1, det2, det3 and det4 tasks to confirm the effectiveness of the mixture model.