Speaker recognition

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

Representation Learning for Speech Emotion Recognition

Abstract: Speech emotion recognition is an important problem with applications as varied as human-computer interfaces and affective computing. Previous approaches to emotion recognition have mostly focused on extraction of carefully engineered features and have trained simple classifiers for the emotion task. There has been limited effort at representation learning for affect recognition, where features are learnt directly from the signal waveform or spectrum. Prior work also does not investigate the effect of transfer learning from affective attributes such as valence and activation to categorical emotions. In this paper, we investigate emotion recognition from spectrogram features extracted from the speech and glottal flow signals; spectrogram encoding is performed by a stacked autoencoder and an RNN (Recurrent Neural Network) is used for classification of four primary emotions. We perform two experiments to improve RNN training : (1) Representation Learning Model training on the glottal flow signal to investigate the effect of speaker and phonetic invariant features on classification performance (2) Transfer Learning RNN training on valence and activation, which is adapted to a four emotion classification task. On the USC-IEMOCAP dataset, our proposed approach achieves a performance comparable to the state of the art speech emotion recognition systems.

ALIZE 3.0-Open Source Toolkit for State-of-the-Art Speaker Recognition

Abstract: ALIZE is an open-source platform for speaker recognition. The ALIZE library implements a low-level statistical engine based on the well-known Gaussian mixture modelling. The toolkit includes a set of high level tools dedicated to speaker recognition based on the latest developments in speaker recognition such as Joint Factor Analysis, Support Vector Machine, i-vector modelling and Probabilistic Linear Discriminant Analysis. Since 2005, the performance of ALIZE has been demonstrated in series of Speaker Recognition Evaluations (SREs) conducted by NIST and has been used by many participants in the last NIST-SRE 2012. This paper presents the latest version of the corpus and performance on the NIST-SRE 2010 extended task.

An Information Theoretic Approach to Speaker Diarization of Meeting Data

Abstract: A speaker diarization system based on an information theoretic framework is described. The problem is formulated according to the information bottleneck (IB) principle. Unlike other approaches where the distance between speaker segments is arbitrarily introduced, the IB method seeks the partition that maximizes the mutual information between observations and variables relevant for the problem while minimizing the distortion between observations. This solves the problem of choosing the distance between speech segments, which becomes the Jensen-Shannon divergence as it arises from the IB objective function optimization. We discuss issues related to speaker diarization using this information theoretic framework such as the criteria for inferring the number of speakers, the tradeoff between quality and compression achieved by the diarization system, and the algorithms for optimizing the objective function. Furthermore, we benchmark the proposed system against a state-of-the-art system on the NIST RT06 (rich transcription) data set for speaker diarization of meetings. The IB-based system achieves a diarization error rate of 23.2% compared to 23.6% for the baseline system. This approach being mainly based on nonparametric clustering, it runs significantly faster than the baseline HMM/GMM based system, resulting in faster-than-real-time diarization.

Segmentation of speech using speaker identification

Abstract: This paper describes techniques for segmentation of conversational speech based on speaker identity. Speaker segmentation is performed using Viterbi decoding on a hidden Markov model network consisting of interconnected speaker sub-networks. Speaker sub-networks are initialized using Baum-Welch training on data labeled by speaker, and are iteratively retrained based on the previous segmentation. If data labeled by speaker is not available, agglomerative clustering is used to approximately segment the conversational speech according to speaker prior to Baum-Welch training. The distance measure for the clustering is a likelihood ratio in which speakers are modeled by Gaussian distributions. The distance between merged segments is recomputed at each stage of the clustering, and a duration model is used to bias the likelihood ratio. Segmentation accuracy using agglomerative clustering initialization matches accuracy using initialization with speaker labeled data. >